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    A data-driven approach based on deep neural networks for lithium-ion battery prognostics
    (Springer London Ltd, 2021) Kara, Ahmet
    Remaining useful life estimation is gaining attention in many real-world applications to alleviate maintenance expenses and increase system reliability and efficiency. Deep learning approaches have recently provided a significant improvement in the estimation of remaining useful life (RUL) and degradation progression concerning machinery prognostics. This research presents a new data-driven approach for RUL estimation using a hybrid deep neural network that combines CNN, LSTM, and classical neural networks. The presented CNN-LSTM neural network aims to extract the spatio-temporal relations in multivariate time series data and capture nonlinear characteristics to achieve better RUL prediction accuracy. To improve the proposed model's performance, PSO is handled to simultaneously optimize the hyperparameters of the network consisting of the number of epochs, the number of convolutional and LSTM layers, the size of units (or filters) in each convolutional, and LSTM layers. Besides, the proposed model in this paper, called the CNN-LSTM-PSO, realizes the multi-step-ahead prediction. In the experimental studies, the popular lithium-ion battery dataset presented by NASA is selected to verify the CNN-LSTM-PSO approach. The experimental consequences revealed that the presented CNN-LSTM-PSO model gives better results than other state-of-the-art machine learning techniques and deep learning approaches considering various performance criteria.
  • Küçük Resim
    Öğe
    Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm
    (SPRINGER, 2024) Kara, Ahmet
    The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are signifcant to prevent the coronavirus’s spread and ensure more efective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more efective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework’s hyperparameters are tuned by means of a genetic algorithm. The efectiveness of the proposed framework has been examined utilizing a public dataset including 18 diferent blood fndings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates.
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    Öğe
    Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm
    (Pergamon-Elsevier Science Ltd, 2021) Kara, Ahmet
    Influenza epidemic is a serious public health problem that has attracted worldwide attention due to cause cases of severe illness, an enormous economic burden, and even deaths worldwide each year. Forecasting influenza outbreak in advance has great significance on influenza-like illness (ILI) prevention and healthcare management. Existing research approaches based on traditional statistical and machine learning have failed to select superior features that detect sophisticated and non-linear characteristics of influenza epidemic sequential data. In this paper, it is introduced a hybrid method that combines long short-term memory (LSTM) neural network and genetic algorithm (GA) for multi-step influenza outbreak forecasting problems. LSTM model is employed to overcome the complexity and nonlinearity issues in an influenza prediction. In order to enhance the efficiency and performance of the neural network, the genetic algorithm is used to obtain the epoch size of the network, the number of LSTM layers, the size of units in each LSTM layer, and the time window size simultaneously. For comparison purposes, it is chosen the weekly data of influenza-like illness (ILI), also known as influenza or other similar illness showing flu-like symptoms, in the USA collected by the Centers for Disease Control and Prevention (CDC). The experimental results demonstrated that the presented hybrid model outperforms other highly developed machine learning approaches, a statistical model, and a fully-connected neural network considering different performance metrics during peak periods.
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    Öğe
    Uzun-Kısa Süreli Bellek Ağı Kullanarak Global Güneş Işınımı Zaman Serileri Tahmini
    (2019) Kara, Ahmet
    Global güneş ışınımı tahmini, güneş enerjisi sistemlerinin etkin yönetimi ve işletilmesinin yanı sıra gelecekteki enerji üretimi hakkında güvenilir bilgi sağlamak için giderek daha fazla önem kazanmaktadır. Bu çalışmada, günlük güneş ışınım tahmin problemini etkin bir model oluşturmak için Uzun Kısa Süreli Bellek (Long Short-Term Memory - LSTM) ağı önerilmiştir. Önerilen yöntemin etkinliği Karar Ağaçları Regresyon, Rastgele Orman Regresyon, Gradyan Güçlendirme ve K-En Yakın Komşu gibi en etkili makine öğrenme algoritmalar ile karşılaştırılmıştır. LSTM modelinin yaklaşımının etkinliğini doğrulamak için Çorum - Türkiye’de Temmuz-1983 ve Aralık-2018 tarihleri arasında global güneş ışınımı sıralı zaman serileri verileri kullanılmıştır. Simülasyon sonuçları, LSTM yönteminin diğer makine öğrenme modellerinden daha iyi performansa sahip olduğunu göstermektedir.