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Lung diseases are indeed the lung-affecting diseases which impair the respiratory mechanism. Lung cancer has been one of the leading causes of mortality in humans worldwide. Early detection can enhance survival chances amid humans. If the condition is diagnosed in time, the average survival rates for people with lung cancer rise from 14 to 49 percent. While computed tomography (CT) is far more effective than X-ray, a thorough diagnosis includes multiple imaging approaches to support each other. A deep neural network for detecting lung cancer from CT images is developed and evaluated. For the classification of the lung image as normal or malignant, a densely connected convolution neural network (DenseNet) and adaptive boosting algorithm wasused. A dataset of 201 lung images is used in which 85% of the images are used for training and 15% of the images are used for testing and classification. Experimental results showed that the proposed method achieved an accuracy of 90.85%. Keywords: DenseNet, Image Processing, Deep Learning, Convolution Neural Networks (CNN).

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The teaching-learning process is seeing a big transformation in this digital age. It involves digital classrooms with various accessories of online tools such as video conferencing, digital materials, and other platforms for learning and assessment with options for both real-time and self-paced work in addition to the availability of teachers over video conferencing, text, phone, email, etc. To improve the online learning efficiency, assessing the cognitive state during the learning phase is highly required for the success of these developments. This work focused on cognitive state analysis during different learning tasks is determined by EEG brain signals that are captured using 128 channels Emotive Epoch headset device. Artifacts prominent in raw signals are filtered by linear filtering. Feature extraction for determination of concentration levels is done by applying fuzzy fractal dimension measures and Discrete Wavelet Transform (DWT) on the processed signals. The classification of extracted parameters into concentration levels is done by using deep learning algorithms like Enhanced Convolutional Neural Network (ECNN). This ECNN deep learning classification is highly accurate amongst all other remaining classifiers and is used as a feedback model to regulate this cognitive state.