Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques.

/pdf/pigeon-inspired-optimization-with-encryption-based-secure-25ha2cga.pdf

Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

A huge amount of data is available to the web users with evolution of web technology. The available resources in web are used by the users and also they involve in giving feedbacks and thus generate additional information. It is very essential to explore, analyze and organize their opinions and feedbacks in an efficient way for better decision making. So sentiment analysis (SA) is used to find the users opinion about a particular topic, product or problem in a more efficient way. The main aim of SA is to solve the problems in relation to opinions about products, movies, politics, review sites etc. Sentiment analysis can be done with different modalities by taking inputs from text, image, audio and video. This paper proposes a system that will automatically recognise the facial expression from the image and classify emotions for final decision. The system uses a simplified method called ‘Viola Jones Face Detection’ algorithm for face localization. Then facial features are extracted using three methods ‘Zernike moments’, ‘LBP’ and ‘DCT transform’. The different feature vectors are combined together using a subset feature selection algorithm to improve the performance of recognition and classification process. Finally the combined features are trained and classified using SVM, Random Forest and KNN classifier. Experiments are conducted on JAFFE database and finally performance of the system is evaluated.

Facial expression recognition and emotion classification system for sentiment analysis

Robust SVD-based schemes for medical image watermarking

Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

https://www.mdpi.com/1999-4893/10/2/70/pdf

An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification

A novel trust-based security and privacy model for Internet of Vehicles using encryption and steganography

A DWT-SVD based robust digital watermarking for medical image security

The goal of this work is to protect as much as possible the images exchanged in telemedicine, to avoid any confusion between the patient’s radiographs, these images are watermarked with the patient’s information as well as the acquisition data. Thus, during the extraction, the doctor will be able to affirm with certainty that the images belong to the treated patient. The ultimate goal of our completed work is to properly integrate the watermark with as little distortion as possible to typically retain the medical information in the image. In this innovative approach used DWT decomposition is appropriately applied to the image which allows a remarkably satisfactory adjustment during the insertion. An SVD is then applied to the three subbands LL, LH and HL, which ideally allows retaining the maximum energy of the used image in a guaranteed minimum of singular values. A specific combination of the three resulting singular value matrices is then performed for watermark integration. The proposed approach ensures data integrity, patient confidentiality when sharing data, and robustness to several conventional attacks.

/pdf/a-lossless-dwt-svd-domain-watermarking-for-medical-1tcew8pa65.pdf

A lossless DWT-SVD domain watermarking for medical information security

In order to improve the security of images exchanged in telemedicine, we propose in this paper, 2 watermarking schemes for retinal image protection; each scheme will be declined into 2 variants. The integration of the watermark will be performed by combining the parity of the successive values; each variant will represent a different combination. These approaches will be implemented in the three insertion domains: spatial, frequency and multi-resolution domain. For the spatial domain, the watermark will be integrated into the R, G and B values of the image. In the frequency domain, the watermark bits will be substituted to the DCT coefficient’s least significant bit. For the multi-resolution domain insertion, after calculating a DWT, the obtained LL sub-band coefficients will be used for the integration process. After comparing our approaches to the different recent works in the three domains; the obtained results demonstrate that our first proposed approach offers a good imperceptibility for the frequency and spatial domains insertion. However, using 512 × 512 images for our experiments considerably reduces the capacity of our approach in the frequency domain.

A value parity combination based scheme for retinal images watermarking

Neural network classifying method is used in this work to perform facial expression recognition. The processed expressions were the six most pertinent facial expressions and the neutral one. This operation was implemented in three steps. First, a neural network, trained using Zernike moments, was applied to the set of the well known Yale and JAFFE database images to perform face detection. In the second step, detected faces are processed to perform the characterization phase through computed vectors of Zernike moments. At last step, a back propagation neural network was trained to distinguish between the seven emotion's states of a presented face. Finally, method performances were evaluated on the well known JAFEE and YALE database.

/pdf/facial-expression-recognition-using-neural-network-trained-115fx5tdel.pdf

Narima Zermi

Papers

A DWT-SVD based robust digital watermarking for medical image security

Robust SVD-based schemes for medical image watermarking

A lossless DWT-SVD domain watermarking for medical information security

A value parity combination based scheme for retinal images watermarking

Facial Expression Recognition Using Neural Network Trained with Zernike Moments