A hybrid egocentric video summarization method to improve the healthcare for Alzheimer patients

Alzheimer patients face difficulty to remember the identity of persons and performing daily life activities. This paper presents a hybrid method to generate the egocentric video summary of important people, objects and medicines to facilitate the Alzheimer patients to recall their deserted memories. Lifelogging video data analysis is used to recall the human memory; however, the massive amount of lifelogging data makes it a challenging task to select the most relevant content to educate the Alzheimer’s patient. To address the challenges associated with massive lifelogging content, static video summarization approach is applied to select the key-frames that are more relevant in the context of recalling the deserted memories of the Alzheimer patients. This paper consists of three main modules that are face, object, and medicine recognition. Histogram of oriented gradient features are used to train the multi-class SVM for face recognition. SURF descriptors are employed to extract the features from the input video frames that are then used to find the corresponding points between the objects in the input video and the reference objects stored in the database. Morphological operators are applied followed by the optical character recognition to recognize and tag the medicines for Alzheimer patients. The performance of the proposed system is evaluated on 18 real-world homemade videos. Experimental results signify the effectiveness of the proposed system in terms of providing the most relevant content to enhance the memory of Alzheimer patients

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach