The article forecasts emerging cyber-risks from the integration of AI in cybersecurity. 

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Super-forecasting the ‘technological singularity’ risks from artificial intelligence

Over recent years the focus on the comprehensive health-care system in IoT has become increasingly important, which considers in many ways a significant concept to promote health-care. It plays a positive role in increasing the highlight of the issue of medical disadvantage that threatens the medical diagnosis. Medical image constitutes a crucial carrier of the patient's diagnosis information. It nonetheless is exposed to several kinds of noise through transmission, and storage, which leads to impeding the full diagnosis for the patient and a loss of its quality as a medical digital image. Noise is a key factor in decreasing the image quality of different sorts of medical images (X ray, CAT scan, and MRI). Many techniques have been applied for image de-noising. The Discrete wavelet transform which is regarded as the most recent and optimum technique. This paper has been presented four levels of a discrete wavelet transform for the removal of Gaussian noise from several medical images based on diverse wavelet family transforms and median filtering. The proposed method submitted admissible results with regard to removing noise from medical images. The performance evaluation of the proposed algorithm is done by measuring the values PSNR, MSD, and NC.

A Novel Gaussian in Denoising Medical Images with Different Wavelets for Internet of Things Devices

Particle Swarm Optimization (PSO) is the global optimization technique that inspires many researchers to solve large scale of non-linear optimization problems. For certain complex scenarios, the premature convergence problem of PSO algorithm cannot find global optimum in dynamic environments. In this paper, a new variant motility factor based Cellular Particle Swarm Optimization (m-CPSO) algorithm is proposed which is developed by the migration behavior observed from fibroblast cellular organism to overcome this problem. The proposed m-CPSO algorithm is modeled in two different social best and individual best models. The performance of m-CPSO is tested in the benchmark and real-time data instances and compared with classical PSO. The outcome of experimental results has demonstrated that m-CPSO algorithm produces promising results than classical PSO on all evaluated environments. Article DOI:  https://dx.doi.org/10.20319/mijst.2017.32.209229 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Cellular organism based particle swarm optimization algorithm for complex non-linear problems

Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

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Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant.

People with several disabilities face many challenges in personal communication with the external real-world environment. Brain Computer Interface (BCI) system offers promising solution for both communication and rehabilitation therapies which overcomes the difficulties faced by disabled people, especially fully non-speaking people. Noninvasive Electroencephalogram (EEG) based BCI system acts as a communication medium that translates brain-activity into commands for computer systems or other devices. EEG signals recorded from the scalp of aforementioned subjects are utilized to extract the meaningful patterns in the EEG-based BCI system. In this paper, an optimized Backpropagation Neural Network (BPN) based on Synergistic Fibroblast optimization (SFO) algorithm is proposed for the classification of EEG patterns in the design of robust BCI system. EEG signals collected from open repository database are employed to evaluate the efficiency of SFO based BPN classifier which is compared with Support Vector Machine (SVM) and conventional BPN method. Investigation of the results show that SFO based BPN method achieves highest classification accuracy compared to other conventional classifiers.

Optimized Backpropagation Neural Network Model For Brain Computer Interface System

Evolution of a new computational model for estimating the time-varying data is a highly empirical task in scientific computing theory. Using various evolutionary computation techniques, the identified parameters and functions of the estimation model are optimized to improve the performance of the execution process. The objective of this paper is to introduce a novel Synergistic Fibroblast Optimization (SFO) algorithm in Kalman filter, to develop an optimal estimation model, for forecasting the future state variables of time series data. The proposed model is evaluated using the water samples collected from Ukkadam Periyakulam Lake, Coimbatore, India, where water quality forecasting is done. Fisher score method is applied to choose optimal features subset from the specified high dimensional dataset. Standard performance metrics such as root mean square error (RMSE), mean absolute error (MAE) and regression equation of Sum of Squared Error (SSE) are measured to evaluate the performance of the estimation model, and it is also compared with the actual measurements. Experimental results illustrate that SFO based estimation model produces better promising results than conventional estimating methods.

A novel synergistic fibroblast optimization based Kalman estimation model for forecasting time-series data

Image analysis involves processing and extraction of knowledge from images which is significantly useful in the large scale of medical and engineering applications. Image denoising is primarily applied in image analysis for degradation of noise, and thus improves the visual quality of images for information retrieval process. In the recent scenarios, due to the increase of complexity and diversity of digital images, removal of noise present in complicated images using classical filters becomes a quite challenge. The objective of this paper is to investigate the performance efficiency of a newly developed Synergistic Fibroblast optimization based Weighted Median Filter (SFO-WMF) for medical image analysis. Experiments are carried out with benchmark images, real time Magnetic Resonance Imaging (MRI) images, ultrasound breast cancer images and compared with conventional filters, namely, mean filter, median filter, wiener filter, Gaussian filter and weighted median filter. The performances of filters are validated using standard performance metrics and computational results demonstrated that the novel filter produces promising results and it outperforms than conventional filters in both qualitative and quantitative perspectives.

G. Jayashree

Papers

A novel synergistic fibroblast optimization based Kalman estimation model for forecasting time-series data

Medical Image Denoising Using Synergistic Fibroblast Optimization Based Weighted Median Filter