Ear Image Recognition using Hyper Sausage Neuron

It is important to distinguish an individual from a group of other individuals to ensure information security an d integrity. One of human body parts that has distinguishable characterics is the ear. Prior attempts on identification of hum an ear image has been implementing statistical pattern recogni tion which focusing more on classification between sample sets . This research attempts to build a robust ear image recognitio n system using Hyper Sausage Neuron (HSN) that concetrates on cognition process rather than classification. A recognition s oftware has been built and tested to recognize ear images. Ear images presented into the software has its geometrical moment invariants extracted. These moments is then used to build a se ven dimensional feature vector which will construct a network of HSN of each individual it represents. Different ear images f rom the same individual is presented into the software to test i ts accuracy. The experiment result shows that ear recognition using HSN has better accuracy and faster training time than p revious recognition attempts using statistical pattern recogniti on

Epidemic modelling by ripple-spreading network and genetic algorithm

Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results

Real-Time Traffic Light Recognition Based on C-HOG Features

This paper proposes a real-time traffic light detection and recognition algorithm that would allow for the recognition of traffic signals in intelligent vehicles. This algorithm is based on C-HOG features (Color and HOG features) and Support Vector Machine (SVM). The algorithm extracted red and green areas in the video accurately, and then screened the eligible area. Thereafter, the C-HOG features of all kinds of lights could be extracted. Finally, this work used SVM to build a classifier of corresponding category lights. This algorithm obtained accurate real-time information based on the judgment of the decision function. Furthermore, experimental results show that this algorithm demonstrated accuracy and good real-time performance

En-PaFlower: An Ensemble Approach using PSO and Flower Pollination Algorithm for Cancer Diagnosis

Machine learning now is used across many sectors and provides consistently precise predictions. The machine learning system is able to learn effectively because the training dataset contains examples of previously completed tasks. After learning how to process the necessary data, researchers have proven that machine learning algorithms can carry out the whole work autonomously. In recent years, cancer has become a major cause of the worldwide increase in mortality. Therefore, early detection of cancer improves the chance of a complete recovery, and Machine Learning (ML) plays a significant role in this perspective. Cancer diagnostic and prognosis microarray dataset is available with the biopsy dataset. Because of its importance in making diagnoses and classifying cancer diseases, the microarray data represents a massive amount. It may be challenging to do an analysis on a large number of datasets, though. As a result, feature selection is crucial, and machine learning provides classification techniques. These algorithms choose the relevant features that help build a more precise categorization model. Accurately classifying diseases is facilitated as a result, which aids in disease prevention. This work aims to synthesize existing knowledge on cancer diagnosis using machine learning techniques into a compact report.  Current research work aims to propose an ensemble-based machine learning model En-PaFlower using Particle Swarm Optimization (PSO) as the feature selection algorithm, Flower Pollination algorithm (FPA) as the optimization algorithm with the majority voting algorithm. Finally, the performance of the proposed algorithm is evaluated over three different types of cancer disease datasets with accuracy, precision, recall, specificity, and F-1 Score etc as the evaluation parameters. The empirical analysis shows that the proposed methodology shows highest accuracy as 95.65%

Highly accurate and reliable ultrasonic focusing capability in heterogeneous media using a spherical cavity transducer

Introduction: Focused ultrasound ablation surgery (FUAS) has been emerging to treat a wide range of conditions non-invasively and effectively with promising therapeutic outcomes. The focusing capability of an ultrasound transducer (i.e., focus shift, beam distortion, and acoustic pressure at the focus) determines the ablation effects. However, the focus shift and focal beam distortion after ultrasound propagating through multi-layered heterogeneous viscoelastic biological tissues become significant and are found to deteriorate the performance of FUAS in clinics.Methods: To achieve an accurate and reliable focal field among patients with large variations in the anatomical structures and properties, a spherical cavity transducer with open ends and sub-wavelength focal size (Li et al., APL, 2013,102:204102) was applied here. Both experimental measurements and numerical simulations were performed to characterize the acoustic fields of the spherical cavity transducer in water, the multi-layered concentric cylindrical phantom, and the heterogeneous tissue model (an adult male pelvis enclosed by porcine skin, fat, and muscle) and then compared with those of a conventional concave transducer at the same electrical power output.Results: It is found that standing-wave focusing using the spherical cavity transducer results in much less focus shift (0.25λ vs. 1.67λ) along the transducer axis and focal beam distortion (−6 dB beam area of 0.71 mm2vs. 4.72 mm2 in water and 2.55 mm2vs. 17.30 mm2 in tissue) in the focal plane but higher pressure focusing gain (40.05 dB vs. 33.61 dB in tissue).Discussion: Such a highly accurate and reliable focal field is due to the excitation at an appropriate eigen-frequency of the spherical cavity with the varied media inside rather than the reverberation from the concave surface. Together with its sub-wavelength focal size, the spherical cavity transducer is technically advantageous in comparison to the concave one. The improved focusing capability would benefit ultrasound exposure for not only safer and more effective FUAS in clinics, but also broad acoustic applications

PMP-SVM: A Hybrid Approach for effective Cancer Diagnosis using Feature Selection and Optimization

Cancer disease is becoming a prominent factor in increasing the death ration over the world due to the late diagnosis. Machine Learning (ML) is playing a vital role in providing computer aided diagnosis models for early diagnosis of cancer. For the diagnosis process the microarray data has its own place. Microarray data contain the genetic information of a patient with a large number of dimensions such as genes with a small sample such as patient details. If the microarray is directly taken without reducing the dimension as the input to any ML model for classification, then Small Sample Size is the resulting issue. So, size of the microarray data needs to be reduces by using either of dimensionality reduction technique or the feature selection technique to increase the model’s performance. In this work, proposed a hybrid model using Principal Component Analysis (PCA), Maximum Relevance Minimum Redundancy (MRMR), Particle Swarm Optimization (PSO) and  Support Vector Machine (SVM) for cancer diagnosis. PCA and MRMR is used for feature selection and PSO is applied to get the optimized feature set. Finally, SVM is applied as the classification model. The proposed model is evaluated against multiple cancer microarray datasets to measure the performance in terms of accuracy, precision, recall, and F1 score. Result shows that proposed model performs better than existing state of art model

Phytomelatonin: Assisting plants to survive and thrive

This review summarizes the advances that have been made in terms of the identified functions of melatonin in plants. Melatonin is an endogenously-produced molecule in all plant species that have been investigated. Its concentration in plant organs varies in different tissues, e.g., roots versus leaves, and with their developmental stage. As in animals, the pathway of melatonin synthesis in plants utilizes tryptophan as an essential precursor molecule. Melatonin synthesis is inducible in plants when they are exposed to abiotic stresses (extremes of temperature, toxins, increased soil salinity, drought, etc.) as well as to biotic stresses (fungal infection). Melatonin aids plants in terms of root growth, leaf morphology, chlorophyll preservation and fruit development. There is also evidence that exogenously-applied melatonin improves seed germination, plant growth and crop yield and its application to plant products post-harvest shows that melatonin advances fruit ripening and may improve food quality. Since melatonin was only discovered in plants two decades ago, there is still a great deal to learn about the functional significance of melatonin in plants. It is the hope of the authors that the current review will serve as a stimulus for scientists to join the endeavor of clarifying the function of this phylogenetically-ancient molecule in plants and particularly in reference to the mechanisms by which melatonin mediates its multiple actions

Heartbeats Do Not Make Good Pseudo-Random Number Generators: An Analysis of the Randomness of Inter-Pulse Intervals

The proliferation of wearable and implantable medical devices has given rise to an interest in developing security schemes suitable for these systems and the environment in which they operate. One area that has received much attention lately is the use of (human) biological signals as the basis for biometric authentication, identification and the generation of cryptographic keys. The heart signal (e.g., as recorded in an electrocardiogram) has been used by several researchers in the last few years. Specifically, the so-called Inter-Pulse Intervals (IPIs), which is the time between two consecutive heartbeats, have been repeatedly pointed out as a potentially good source of entropy and are at the core of various recent authentication protocols. In this work, we report the results of a large-scale statistical study to determine whether such an assumption is (or not) upheld. For this, we have analyzed 19 public datasets of heart signals from the Physionet repository, spanning electrocardiograms from 1353 subjects sampled at different frequencies and with lengths that vary between a few minutes and several hours. We believe this is the largest dataset on this topic analyzed in the literature. We have then applied a standard battery of randomness tests to the extracted IPIs. Under the algorithms described in this paper and after analyzing these 19 public ECG datasets, our results raise doubts about the use of IPI values as a good source of randomness for cryptographic purposes. This has repercussions both in the security of some of the protocols proposed up to now and also in the design of future IPI-based schemes.This work was supported by the MINECO Grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You); by the CAMGrant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data and Risks); and by the MINECO Grant TIN2016-79095-C2-2-R (SMOG-DEV: Security Mechanisms for fog computing: advanced security for Devices). This research has been supported by the Swedish Research Council (Vetenskapsrådet) under Grant No. 2015-04154 (PolUser: Rich User-Controlled Privacy Policies)