Browsing by Author "Rezaeizadeh, R."

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Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Mohamed, A.A.A.; Hançerlioğullari, A.; Rahebi, J.; Rezaeizadeh, R.; Lopez-Guede, J.M.
Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN–Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.
Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer
(2024.01.01) Mohamed, A.A.A.; Hançerliogullari, A.; Rahebi, J.; Rezaeizadeh, R.; Lopez-Guede, J.M.
Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.
Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model
(2023.01.01) Abusida, A.M.; Karatay, S.; Rezaeizadeh, R.; Hancerliogullari, A.
The main goal of this study is to compare the performance of the classification algorithms applied to the SCADA database of the Supervisory Control and Data Acquisition (SCADA) system of the General Electricity Company of Libya (GECOL). The company's annual energy and material losses have become seriously important to the Libyan government's research field. The well-established data mining and classification software package known as the WEKA tool is used to minimize these losses,. As necessary data input for algorithms; six different parameters are taken into consideration, namely power production size, energy production duration, energy production date, ambient temperature, humidity level and wind speed. This study is examined in detail for the first time in this article. In addition, considering the temperature variables, humidity, wind and other atmospheric effects of the environment, the energy losses of the company and the country are reduced to a minimum level. As a result, the company's annual electricity consumption is classified as low, medium or high consumption with the simulations. Thus, in cases where energy consumption is high, it is possible to make accurate and rapid decisions regarding the determination and classification of time periods related to energy consumption.
IMPROVING THERMAL AND ELECTRICITY GENERATION PERFORMANCE OF PHOTOVOLTAIC/THERMAL (PV/T) SYSTEMS USING HYBRID NANOFLUID
(2024.01.01) Swese, E.E.O.; Sözen, A.; Rezaeizadeh, R.; Hançerliogullari, A.; Aytaç, I.; Variyenli, H.I.; Çakir, M.T.
Solar energy is a safe and clean source of energy, available on the Earth throughout the year. A photovoltaic/thermal (PV/T) system is a device designed to take solar energy and convert it into electrical/thermal energy. Photovoltaic/ thermal systems can also be useful to produce hot fluid (usually water) along with the generation of electrical energy. In addition, the electric generating performance of PVs increases with heat discharging ability of thermal system, which also prevents overheating in PV systems. Nanofluid is a new generation heat transfer fluid that delivers higher thermal conductivity and heat transfer rate compared to conventional fluids. The thermal conductivity of the nanofluid depends on the size of the nanoparticles, concentration of the nanofluid, and the method of its preparation. In this study, it is aimed to increase the thermal heat transfer of the PV/T system by using hybrid nanofluids, manufactured by adding 0.5% Fe2O3 and Fe3O4 nanoparticles to the water as a working fluid. By using hybrid nanofluids, increase in bidirectional performance along with enhanced cooling is achieved. In the experimental study, more heat was withdrawn from the heated PV panels by utilizing the high thermal conductivity of the hybrid nanofluid, and the best improvement in total efficiency was obtained as 86% for the hybrid nanofluid. With the use of hybrid nanofluids in the cooling circuit, the electrical and thermal efficiency of the PV panel has reached to overall 81% on average basis.