The need for cloud servers for training deep neural network (DNN) models is increasing as more complex architecture designs of DNN models are developed. Nevertheless, cloud servers are considered semi-honest. With great attention to the privacy issues of medical diagnoses using a DNN, previous studies have proposed the idea of learnable image encryption. Though some methods have been presented to partially attack previous encryption schemes, there is still some space for improvement. We proposed a learnable image encryption scheme that is an enhanced version of previous methods and can be used to train a great DNN model and simultaneously keep the privacy of training images. We conducted an experiment on medical datasets from open sources and the result demonstrates the effectiveness of our proposed method in performance and privacy-preserving.

Cyberattacks are increasing in both number and severity for private, corporate, and governmental bodies. To prevent these attacks, many intrusion detection systems and intrusion prevention systems provide computer security by monitoring network packets and auditing system records. However, most of these systems only monitor network packets rather than the computer itself, so physical intrusion is also an important security issue. Furthermore, with the rapid progress of the Internet of Things (IoT) technology, security problems of IoT devices are also increasing. Many IoT devices can be disassembled for decompilation, resulting in the theft of sensitive data. To prevent this, physical intrusion detection systems of the IoT should be considered. We here propose a physical security system that can protect data from unauthorized access when the computer chassis is opened or tampered with. Sensor switches monitor the chassis status at all times and upload event logs to a cloud server for remote monitoring. If the system finds that the computer has an abnormal condition, it takes protective measures and notifies the administrator. This system can be extended to IoT devices to protect their data from theft.

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This study presents a compact L-points discrete cosine transform (DCT) hardware accelerator for M-points Mel-scale Frequency Cepstral Coefficients (MFCC). The main contributions of this work can be summarized as 1) proposing an algorithm with lower complexity; 2) achieving higher accuracy performance; 3) implementing a low-cost accelerator with a unique group of cosine coefficients. For algorithm derivation, the proposed method converts the original formula into the type IV of discrete cosine transform (DCT-IV) with a preprocessing procedure. The kernel computation of DCT-IV can be further derived into the same cosine multiplication with the proposed preprocessing. Therefore, a total of (M-1) (L-1) additions, (M-1) L multiplications, and L coefficients are required for the computation. Compared with Jo et al.'s algorithm, the proposed method respectively reduces the number of additions and multiplications by 42.32 % and 41.67 %. Instead, the number of coefficients is increased by 33.33 %. Moreover, the proposed algorithm exhibits a higher peak signal-to-noise ratio (PSNR) value which is achieved at 90.1dB with a 16bit coefficient word length. For hardware realization, the FPGA implementation results show that it can operate at a clock rate of 135.85 MHz and requires only 113 combinational elements, 87 registers, 3 DSP multipliers, 64×16 bits RAM and 32×16 bits ROM. Overall, it would be a good choice for integrating MFCC applications in the future.INDEX TERMS discrete cosine transform (DCT); Hardware Accelerator; Mel-scale Frequency Cepstral Coefficients (MFCC)

Utilizing supervised machine learning algorithms to develop a surveillance and response system based on symptoms of diarrhoea, contingent on the Support Vector Machine (SVM) to predict the probable disease using labelled data. Diarrhoea is amongst the top ten diseases which kill. A prototype system is developed based on the SVM algorithm. The prototype system takes six patient symptoms that which is input, from the user and the output result becomes the prognosis which may likely occur based solely on the given symptoms. Two other supervised learning models have been utilized in the prediction process, Random Forest Model (RFC) and Naïve Bayes Model (NB). Furthermore, a visualization on google maps (my maps) on the area in which a diarrhoea outbreak would likely occur. The constituency and the region of the patient will be used to place a pin on my maps, giving a visualization on the map, with a mapping structure this allows for a vivid demonstration of how diarrhoea is spreading in Eswatini. SVM received an average of 100% accuracy. The other two supervised learning models, random forest model and naïve Bayes model received 97.62% average accuracy on the same dataset. It shows that the SVM does well in data classification and with a small dataset.INDEX TERMS Diarrhoea, prognosis, supervised machine learning. I. INTRODUCTIONThis research explores ways in which acute diarrhoea can be detected at early stages within communities in Eswatini to widely reduce the chances of an outbreak, mostly in children under the age of 5. When it comes to experiencing acute diarrhoea [7] it should be short-lived. When acute diarrhoea spans over weeks, there is a major concern.The World Health organization defines acute diarrhoea ''as the passage of three or more loose or liquid stools per day'', [7]. There has been nearly 1.7 billion annually recorded global cases of childhood diarrhoeal diseases [7]. A Global Burden of Disease Study which was conducted in 2017 showed that, there have been 22167 total deaths due to diarrhoea for the past 27 year, from 1990 to 2017. The death toll of children under 5 has been 12454 in these years.The associate editor coordinating the review of this manuscript and approving it for publication was Yudong Zhang .