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Plant diseases are a major threat to food security and economic prosperity around the globe. Deep learning models based on Convolution Neural Network (CNN) have shown promising results in dealing with plant disease detection tasks. However, according to the No Free Lunch Theorem, no single model is suitable for all cases. Moreover, the vast diversity of plant diseases makes the model selection process time and resource extensive, using exhaustive search. This work proposes a meta-learning-based framework that recommends top-n suitable models for an unseen plant disease detection dataset using the prior evaluations of benchmark models on plant disease detection tasks. Rank-Biased Overlap (RBO) is used to evaluate the efficacy of the proposed framework by evaluating actual rankings with respect to the predicted rankings. Extensive comparative experiments are carried out with different configurations of meta-extractors and meta-learners. The results obtained demonstrate that the probe network trained for 10 epochs (termed as "intermediate stage") along with standard deviation as meta-extractor and Support Vector Regressor as the meta-learner outperforms the rest with average RBO scores of 0.76, 0.73 and 0.75 for Top-5, Top-3 and Top-1 recommendations, respectively. Overall, this paper presents a viable substitute for the exhaustive search process carried out for choosing the best deep learning model for plant disease detection scenario, leading to better resource utilization and faster implementation procedure. • Meta-learning framework to recommend models for plant disease identification task. • Meta-dataset created using 13 benchmark CNN models on 24 different species of plants. • RBO score used to evaluate combinations of meta-extractors and meta-learners. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Summary: In the development of smart cities, the intelligent transportation system (ITS) plays a major role. The dynamic and chaotic nature of the traffic information makes the accurate forecasting of traffic flow as a challengeable one in ITS. The volume of traffic data increases dramatically. We enter the epoch of big data. Hence, a 1deep architecture is necessary to process, analyze, and inference such a large volume of data. To develop a better traffic flow forecasting model, we proposed an attention‐based convolution neural network long short‐term memory (CNN‐LSTM), a multistep prediction model. The proposed scheme uses the spatial and time‐based details of the traffic data, which are extracted using CNN and LSTM networks to improve the model accuracy. The attention‐based model helps to identify the near term traffic details such as speed that is very important for predicting the future value of flow. The results show that our attention‐based CNN‐LSTM prediction model provides better accuracy in terms of prediction during weekdays and weekend days in the case of peak and nonpeak hours also. We used data from the largest traffic data set the California Department of Transportation (Caltrans) for our prediction work. [ABSTRACT FROM AUTHOR]

Due to Internet of Things (IoT), it has become easy to surveil the critical regions. Images are important parts of Surveillance Systems, and it is required to protect the images during transmission and storage. These secure surveillance frameworks are required in IoT systems, because any kind of information leakage can thwart the legal system as well as personal privacy. In this paper, a secure surveillance framework for IoT systems is proposed using image encryption. A hyperchaotic map is used to generate the pseudorandom sequences. The initial parameters of the hyperchaotic map are obtained using partial-regeneration-based non-dominated optimization (PRNDO). The permutation and diffusion processes are applied to generate the encrypted images, and the convolution neural network (CNN) can play an essential role in this part. The performance of the proposed framework is assessed by drawing comparisons with competitive techniques based on security parameters. It shows that the proposed framework provides promising results as compared to the existing techniques. [ABSTRACT FROM AUTHOR]