Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

The science of the total environment

International Journal of Computer and Applications

IEEE Antennas and Wireless Propagation Letters

The world's most widely used comprehensive reference book of medicine has been updated 16 times since 1899. It took 5 years of work to bring out this 16th edition. The 12 physicians on the editorial board enlisted the aid of 29 distinguished consultants with outstanding qualifications to complete the volume. The manual has been expanded by 120 pages and includes 154 figures and 382 tables. New subjects include sports medicine, hospice medicine, human immunodeficiency virus infection in children, cardiopulmonary resuscitation in infants and children, genetic evaluation and counseling, anabolic steroid abuse, cultural issues in medicine, and cocaine withdrawal in newborns. A table of generic drugs with their trade names has been included and will be useful for the ophthalmologist as a reference. As in past editions, the contents are arranged by organ system on the basis of the origins of each disease. The pathologicand physiologic features of each disease are

https://jamanetwork.com/journals/jamaophthalmology/articlepdf/640584/archopht_112_3_018.pdf

The Merck Manual of Diagnosis and Therapy

In the past decade, image encryption has emerged as a matter of interest to scientists and researchers. The paper presents a synchronous permutation-diffusion image encryption algorithm using Intertwining Logistic Map (ILM) and Deoxyribonucleic acid (DNA) for color images. The design of the proposed algorithm is simple, secure and efficient. It is mainly composed of two phases: Permutation and Diffusion. In the first phase, the original image pixels are permuted using the 3D chaotic sequence generated by ILM. In the next phase, the permuted pixels from step one are diffused using DNA XOR operation. The implemented approach is faster as permutation and diffusion operations are performed synchronously in a single iteration. The paper evaluates the performance of the proposed approach using various parameters like key sensitivity, entropy, secret key space, contrast analysis, correlation coefficient (CC)—vertical, horizontal and diagonal, Number of Pixel Change Rate (NPCR) and Unified Average Change in Intensity (UACI). The evaluated results show that use of DNA and synchronization feature with ILM exhibits significant improvement in the information entropy, increase in the randomness feature and contrast, high resistance against the varied statistical and differential attacks, and improvement in encryption efficiency.

A synchronous intertwining logistic map-DNA approach for color image encryption

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction. General Terms Information and decision systems, (in)discernibility , approximation spaces, rough sets, rough membership functions, reducts, decision rules, dependencies of attributes. Clustering, Rule Induction, Feature Selection

/pdf/rough-set-approach-in-machine-learning-a-review-2vsfcgfb52.pdf

Rough Set Approach in Machine Learning: A Review

Background: Voice, apart from its semantic content also carries information about the speaker’s psychological and physical state. Emotional stress or physical fatigue, are the pathological elements of this condition. The possible relationship between emotional stress and the measurable changes to the voice signal was the subject of this study. Method: Eleven subjects were interviewed with questions from two domains and their responses were recorded. In the first domain, two men, two women and three teenagers were asked to remember an incident from their past where they felt embarrassed or ashamed of their own act. In the second domain, three women and one man from the house keeping staff were interviewed for the stolen mobile phone. These subjects were different from the subjects who participated in domain 1. Stress in voice was detected as a measure of shift in the acoustic parameters with respect to the baseline. All recordings were analyzed using PRAAT software. Spectrograms were also plotted for qualitative comparison between normal speech and stressed speech. Result: Significant increase in mean pitch and substantial decrease in the first two formants (F1 and F2) were observed under stress. Other acoustic measures did undergo change under stress but failed to reveal any significance. Spectrograms were distinct for the two conditions. Conclusion: Obtained results indicate that, when a person is emotionally charged, stress could be discerned in his voice. Mean pitch and Formants F1 and F2 have been obtained as reliable vocal indicators of emotional stress. This study proposes a simple non-invasive approach which can act as an alibi for innocent people.

/pdf/vocal-indicators-of-emotional-stress-3nndv7lo6k.pdf

Vocal Indicators of Emotional Stress

Evolutionary algorithms are generally a suitable approach for optimization problems, having more than one conflicting objectives. For many complicated engineering optimization problems, multi-objective formulations are treated as realistic models. The paper presents and implements a Pareto-optimal image encryption algorithm that uses coupled map lattice (CML) chaos function and deoxyribonucleic acid (DNA) combination to encrypt an image. The discussed work uses multi-objective genetic algorithm (MOGA) to get the optimized results. The proposed two-step algorithm uses pseudo-random number generators, the chaotic method CML and DNA to create an initial population of DNA masks in its initial stage. The MOGA is applied in the second stage to obtain the best mask for encrypting the given plain image. The focus is on the generation of Pareto fronts by using the Pareto generation method of multi-objective optimization. The paper evaluates the performance of the implemented work using standard metrics like key sensitivity, secret key space, number of pixel change rate, unified average changed intensity, entropy, histogram and correlation coefficient. It also discusses the impact of using a genetic algorithm that uses more than one fitness function as the objective for encrypting images.

A Pareto-optimal evolutionary approach of image encryption using coupled map lattice and DNA

Breast cancer is one of the leading causes of cancer deaths among women in developed countries including India. Mammography is currently the most effective method for detection of breast cancer. Early diagnosis of the breast cancer allows treatment which could lead to high survival rate. This paper presents breast cancer detection in digital mammography using Image Processing Techniques by Artificial Neural Networks. A clinical database of 42 previously verified patient cases are employed and randomly partitioned into two independent sets for training and testing. Gray Level Co-occurrence Matrix (GLCM) features extracted from the known Mammogram images are used to train Artificial Neural Network based detection system. In Testing/Recognition Phase the extracted features of known and unknown Mammogram images are compared for classification of images into malignant and benign. Feed-forward back propagation and Cascade-forward back propagation Artificial Neural Network structures had been trained for detection. The performance is evaluated on the basis of Mean Square Error (MSE) and accuracy of both the structure has been compared.

Ritu Vijay

Papers

A synchronous intertwining logistic map-DNA approach for color image encryption

Rough Set Approach in Machine Learning: A Review

Vocal Indicators of Emotional Stress

A Pareto-optimal evolutionary approach of image encryption using coupled map lattice and DNA

Mammogram Analysis Using Feed-Forward Back Propagation and Cascade-Forward Back Propagation Artificial Neural Network