In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.
Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease identification and classification is proposed. Secondly, we fine-tune VGG and pruned ResNets and utilize them as feature extractors and connect them to fully connected dense networks. The hyperparameters are tuned to reach faster convergence and obtain better generalization during stochastic optimization of Bi-CNN(s). Finally, the proposed model is designed to leverage scalability by implying the Bi-CNN model into a real-world application and release it as an open-source. The model is designed on variant testing criteria ranging from 10% to 50%. These models are evaluated on gold-standard classification measures. To study the performance, testing samples were expanded by 5x (i.e., from 10% to 50%) and it is found that the deviation in the accuracy was quite low (0.27%) which resembles the consistent generalization ability. Finally, the larger model obtained an accuracy score of 94.98% for 38 distinct classes.
Arrhythmia is ubiquitous worldwide and cardiologists tend to provide solutions from the recent advancements in medicine. Detecting arrhythmia from ECG signals is considered a standard approach and hence, automating this process would aid the diagnosis by providing fast, costefficient, and accurate solutions at scale. This is executed by extracting the definite properties from the individual patterns collected from Electrocardiography (ECG) signals causing arrhythmia. In this era of applied intelligence, automated detection and diagnostic solutions are widely used for their spontaneous and robust solutions. In this research, our contributions are two-fold. Firstly, the Dual-Tree Complex Wavelet Transform (DT-CWT) method is implied to overhaul shift-invariance and aids signal reconstruction to extract significant features. Next, A neural attention mechanism is implied to capture temporal patterns from the extracted features of the ECG signal to discriminate distinct classes of arrhythmia and is trained end-to-end with the finest parameters. To ensure that the model's generalizability, a set of five traintest variants are implied. The proposed model attains the highest accuracy of 98.5% for classifying 8 variants of arrhythmia on the MIT-BIH dataset. To test the resilience of the model, the unseen (test) samples are increased by 5x and the deviation in accuracy score and MSE was 0.12% and 0.1% respectively. Further, to assess the diagnostic model performance, AUC-ROC curves are plotted. At every test level, the proposed model is capable of generalizing new samples and leverages the advantage to develop a real-world application. As a note, this research is the first attempt to provide neural attention in arrhythmia classification using MIT-BIH ECG signals data with state-of-the-art performance.
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