Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient’s data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.

/pdf/secure-medical-data-transmission-model-for-iot-based-57stlk1cx6.pdf

Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

https://www.mdpi.com/2077-0383/9/3/674/pdf

Optimization method for forecasting confirmed cases of COVID-19 in China

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort

Improved salp swarm algorithm based on particle swarm optimization for feature selection

The development of the Internet of Things (IoT) is predicted to change the healthcare industry and might lead to the rise of the Internet of Medical Things. The IoT revolution is surpassing the present-day human services with promising mechanical, financial, and social prospects. This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies. For the most part, the patient data are stored as a cloud server in the hospital due to which the security is vital. So another framework is required for the secure transmission and effective storage of medical images interleaved with patient information. For increasing the security level of encryption and decryption process, the optimal key will be chosen using hybrid swarm optimization, i.e., grasshopper optimization and particle swarm optimization in elliptic curve cryptography. In view of this method, the medical images are secured in IoT framework. From this execution, the results are compared and contrasted, whereas a diverse encryption algorithm with its optimization methods from the literature is identified with the most extreme peak signal-to-noise ratio values, i.e., 59.45 dB and structural similarity index as 1.

Hybrid optimization with cryptography encryption for medical image security in Internet of Things

The query efficiency of a data structure that stores a set of objects, can normally be assessed by analysing the number of objects, pointers etc. looked at when answering a query. However, if the data structure is too big to fit in main memory, data may need to be fetched from disk. In that case, the query efficiency is easily dominated by moving the disk head to the correct locations, rather than by reading the data itself.

To reduce the number of disk accesses, once can group the data into blocks, and strive to bound the number of different blocks accessed rather than the number of individual data objects read. An R-tree is a general-purpose data structur that stores a hierarchical grouping of geometric objects into blocks. Many heuristics have been designed to determine which objects should be grouped together, but none of these heuristics could give a guarantee on the resulting worst-case query time.

We present the Priority R-tree, or PR-tree, which is the first R-tree variant that always answers a window query by accessing $O((N/B)^{1-1/d} + T/B)$ blocks, where $N$ is the number of $d$-dimensional objects stored, $B$ is the number of objects per block, and $T$ is the number of objects whose bounding boxes intersect the query window. This is provably asymptotically optimal. Experiments show that the PR-tree performs similar to the best known heuristics on real-life and relatively nicely distributed data, but outperforms them significantly on more extreme data.

The Priority R-Tree: A Practically Efficient and Worst-Case-Optimal R-Tree

Due to the advances in computer-based communication and health services over the past decade, the need for image security becomes urgent to address the requirements of both safety and non-safety in medical applications. This paper proposes a new fragile watermarking-based scheme for image authentication and self-recovery for medical applications. The proposed scheme locates image tampering as well as recovers the original image. A host image is broken into $4\times 4$ blocks and singular value decomposition (SVD) is applied by inserting the traces of block wise SVD into the least significant bit of the image pixels to figure out the transformation in the original image. Two authentication bits namely block authentication and self-recovery bits are used to survive the vector quantization attack. The insertion of self-recovery bits is determined with Arnold transformation, which recovers the original image even after a high tampering rate. SVD-based watermarking information improves the image authentication and provides a way to detect different attacked area of the watermarked image. The proposed scheme is tested against different types of attacks such as text removal attack, text insertion attack, and copy and paste attack. Compared with the state-of-the art methods, the proposed scheme greatly improves both tamper localization accuracy and the peak signal to noise ratio of self-recovered image.

Secure and Robust Fragile Watermarking Scheme for Medical Images

Robots have recently gained a great attention due to their potential to work in dynamic and complex environments with obstacles, which make searching for an optimum path on-the-fly an open challenge. To address this problem, this paper proposes a Genetic Algorithm (GA) based path planning method to work in a dynamic environment called GADPP. The proposed method uses Bezier Curve to refine the final path according to the control points identified by our GADPP. To update the path during its movement, the robot receives a signal from a Base Station (BS) based on the alerts that are periodically triggered by sensors. Compared to the state-of-the-art methods, GADPP improves the performance of robot based applications in terms of the path length, the smoothness of the path, and the required time to get the optimum path. The improvement ratio regarding the path length is between 6% and 48%. While the path smoothness is improved in the range of 8% and 52%. In addition, GADPP reduces the required time to get the optimum path by 6% up to 47%.

Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve

This paper proposed an intelligent approach to predict the biochar yield. The biochar is an important renewable energy that produced from biomass thermochemical processes with yields that depend on different operating conditions. There are some approaches that are used to predict the production of biochar such as least square support vector machine. However, this approach suffers from some drawbacks such as get stuck in local point and high time complexity. In order to avoid these drawbacks, the adaptive neuro-fuzzy inference system approach is used and this approach is trained with a particle swarm optimization algorithm to improve the prediction performance of the biochar. Heating rate, pyrolysis temperature, Moisture content, holding time and sample mass were used as the input parameters and the outputs are biochar mass and biochar yield. The results show that the proposed approach is better than other approaches based on three measures the root mean square error, the coefficient of determination and average absolute percent relative error (0.2673, 0.9842 and 3.4529 respectively).

Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization

Nowadays, designing and developing wearable devices that could detect many types of diseases has become inevitable for E-health field. The decision-making of those wearable devices is done by various levels of analysis of enormous databases of human health records. Systems that demand a huge number of input data to decide to require real-time data collected from devices, processes, and analyzing the data. Many researchers utilize the Internet of Things (IoT) in medical wearable devices to detect different diseases by using different sensors together for one goal. The IoT promises to revolutionize the lifestyle using a wealth of new services, based on interactions between large numbers of devices data. The proposed work is human monitor system to track the human body troubles. Smart wearable devices can provide users with overall health data, and alerts from sensors to notify them on their mobile phones accordingly. The proposed system developed a technique using Internet of Things technique to decrease the load on IOT network and decrease the overall cost of the users. The simulation results proved that the proposed system could provide identical communication for IOT devices even if many nodes are used.

Quantified Self Using IoT Wearable Devices

Increasing demand and costs for healthcare is a challenge because of the high populations and the difficulty to cover all patients by the available doctors. The healthcare data processing and management became a challenge because the problems with the data itself like irregularity high-dimensionality, and sparsity. A number of researchers worked on these problems and provided some efficient and scalable healthcare solutions. we present the algorithms and systems for healthcare analytics and applications and some related solutions. The solution what we propose is depending on adding a new layer as middleware between the sources of heterogeneous data and the Map reduce Hadoop cluster. The solution solved the common problems of dealing with heterogeneous data effectively.

Abdulaziz Shehab

Papers

Secure and Robust Fragile Watermarking Scheme for Medical Images

Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve

Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization

Quantified Self Using IoT Wearable Devices

Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and Recommendations