Potentiometric and conductometric studies of malonyl bis(salicyloylhydrazone) and divalent metal complexes

AbstractA series of complexes of divalent transition metal ions with malonyl bis(salicyloylhydrazone) (H4MSH) have been prepared and characterized with the help of conductometric, potentiometric methods. The proton–ligand and metal–ligand stability constants were obtained pH-metrically. The electrical conductivity of solid complexes was measured at 289K. The low molar conductance values observed for these complexes indicate that, they are non-electrolytes. They are soluble to a limited extent in DMF and DMSO. The elemental analyses of the complexes indicate that the complexes have 1:1 and 2:1 (M:L) stoichiometry with the existence of water, chloride, acetone molecules inside the coordination sphere as evidence from the IR spectral studies. Further, the complexes have been formulated by comparing C, H, N & metal analysis data, and UV–visible spectra of the complexes have been discussed. The protonation constants of the ligand and the stability constants of their metal complexes will be evaluated potentiometrically. The stoichiometric ratios of the complexes formed in solution will be evaluated applying the molar ratio (spectrophotometric) method and confirmed conductometrically

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

Neural network architecture called Dynamic Self-organised Multilayer Network Inspired by the Immune Algorithm is proposed for the prediction of weather signals. Two sets of experiments have been implemented. The simulation results showed slight improvement achieved by the proposed network when using the average results of 30 simulations. For the second set of experiments, the simulation results indicated that there is no significant improvement over the first set of experiments. Since clustering methods have been widely used in different applications of data mining, the adaption of unsupervised learning in the proposed network might serve these different applications, for example, medical diagnostics and pattern recognition for big data. The structure of the proposed network can be modified for clustering tasks by changing the back-propagation algorithm in the output layer. This can extend the application of the proposed network to scientifically analyse different types of big data

Pupil Localisation and Eye Centre Estimation using Machine Learning and Computer Vision

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye-frames within the input image. We then use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye-frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along-with a standardized distance metric we introduce first time in this study. Proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare and automated deception detection

Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques. Features are ranked to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the Radial Basis Function Neural Network classifier performed the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate

Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants is most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography – EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organized network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. The proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%

Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using ehg records

Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate. © 2014 Springer International Publishing Switzerland

A Genetic Analytics Approach for Risk Variant Identification to Support Intervention Strategies for People Susceptible to Polygenic Obesity and Overweigh

Obesity is a growing epidemic that has increased steadily over the past several decades. It affects significant parts of the global population and this has resulted in obesity being high on the political agenda in many countries. It represents one of the most difficult clinical and public health challenges worldwide. While eating healthy and exercising regularly are obvious ways to combat obesity, there is a need to understand the underlying genetic constructs and pathways that lead to the manifestation of obesity and their susceptibility metrics in specific individuals. In particular, the interpretation of genetic profiles will allow for the identification of Deoxyribonucleic Acid variations, known as Single Nucleotide Polymorphism, associated with traits directly linked to obesity and validated with Genome-Wide Association Studies. Using a robust data science methodology, this paper uses a subset of the TwinsUK dataset that contains genetic data from extremely obese individuals with a BMI≥40, to identify significant obesity traits for potential use in genetic screening for disease risk prediction. The approach posits a framework for methodical risk variant identification to support intervention strategies that will help mitigate long-term adverse health outcomes in people susceptible to obesity and overweight

Lossy and Lossless Video Frame Compression: A Novel Approach for the High-Temporal Video Data Analytics

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search