The global airline industry serves over two billion traveller annually and faces continuous challenges about luggage handling. Lost luggage, mishandling of luggage are most common problem for the traveller. With the n...
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Nano fish tanks are highly sensitive to temperature fluctuations due to their small water volume, presenting risks to aquatic life. This paper introduces an innovative loT Web-Based Water Temperature Control System th...
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Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising *** the other hand,in medical studies,a limited dataset decreases the a...
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Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising *** the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL *** this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with *** study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between *** comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of *** model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original *** data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,*** developed model using synthetic data obtained a higher accuracy value than the related studies in the ***,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.
In the automation and robotics industry, there is an increasing interest in the magnetization properties of intelligent materials, such as magnetorheological fluids (MR fluids). The article discusses the method of obt...
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The growing prevalence of electric vehicles (EVs) and the installation of charging infrastructure in public parking lots(PL) present an opportunity to utilize EV batteries for participation in electricity markets. In ...
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This paper proposes a novel line voltage-derived formulation to reduce and eliminate third-order harmonics due to uncertainty in DC link voltages of cascaded H -Bridge (CHB) inverters. These converters produce third-o...
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Traditionally, robot control has relied on mathematical solutions, particularly inverse kinematics (IK), to determine the joint angles needed for a desired end-effector pose. However, these solutions face significant ...
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A prominent family of methods for learning data distributions relies on density ratio estimation (DRE), where a model is trained to classify between data samples and samples from some reference distribution. DRE-based...
Lung cancer is a dangerous disease that can be fatal, and a correct diagnosis is essential for figuring out the best way to treat it. The optimum treatment for people with lung cancer requires the classification of th...
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Lung cancer is a dangerous disease that can be fatal, and a correct diagnosis is essential for figuring out the best way to treat it. The optimum treatment for people with lung cancer requires the classification of the disease into its histological types, such as adenocarcinoma (ADC), small cell lung cancer (SCLC), and squamous cell carcinoma (SCC). Each histological subtype has its features and may react differently to different types of medicine. So, knowing the exact subtype helps guide treatment choices and improve the patient's outcome. Lung cancer subtypes are necessary for personalized treatment. It helps doctors choose tumor-specific treatments such as surgery, radiation, chemotherapy, targeted drugs, and immunotherapies. Precise categorization improves prognosis, avoids needless medicines, and lets patients participate in clinical studies targeting their cancer subtype. Precision medicine improves lung cancer outcomes with accurate categorization. The current algorithms in this domain have shown deficiencies in performance criteria such as specificity, F-score, sensitivity, and precision in recognition. These limitations may stem from challenges such as the complexity and heterogeneity of histopathological images, variations in staining techniques, and the presence of confounding factors. Deep learning methods have made it easier to look at histopathology slides of cancer and see what's going on. Several studies have shown that convolutional neural networks (CNN) are essential for classifying histopathological pictures of different kinds of cancer, like brain, skin, breast, lung, and colon cancer. This study divides lung cancer images into three groups: normal, adenocarcinoma, and squamous cell carcinoma. We have been training deep learning algorithms to identify lung cancer in histopathology slides better, and utilizing deep learning strategies and cutting-edge algorithms such as VGG-19, ResNet-50 v2, EfficientNetB1, and others indicates a comprehensive ap
Non-orthogonal multiple access is a feasible radio access solution for cellular networks to assist Internet of Things devices due to scalable connectivity, higher throughput and low latency. In this work, a user selec...
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