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To detect pesticide in Food beverages by Partial Structural Examination of CDs with the help of Fluorescence quenching study
Get PDFIn recent world, there are lots of crimes happen by adding some hazardous or poisonous materials in the food and food beverages. So it is very hard and cost effective to detect those materials. By one step synthesising carbon dots, the hydrothermal method is very accurate and precise. Carbon dots were synthesised by using hydrothermal methods with different precursors (ethanolamine, urea, ammonium thiocynate) while synthesising. Fluorescence quenching study has been done by using spectrofluorophotometer by adding some amount of food beverage in carbon dots solution. The structural property of CDs and the fluorescence quenching study have been examined by UV-vis spectrometer, atomic force microscopy, spectrofluorophotometer and FT-IR. The comparisons of precursors conclude that ethanolamine is the accurate and precise precursor to synthesise carbon dots with limited crystalline size (30nm-40nm). It shows better and less time consuming detection of the pesticide in appy fizz drink. By using ethanolamine batch, the fluorescence graph gradually decreased by adding 20ppm carbon dot solution after each reading.  
Top 5 Enablement Engineering Capabilities for Enterprise AI and Machine Learning in North American Banks
Get PDFBanks in North America are going through two consecutive transformations, first is digital transformation, and second is the transformation towards AI-powered banking. However, each bank can be in different stage of the overall changes while the challenges surfaced in COVID-19 pandemic intensify the second. Applications of these AI technologies in banks or financial institutions of significant size, are referred to as Enterprise AI, which are used in providing technology solutions or solving business problems at the enterprise scale. In this short paper, the author will present five engineering enablement capabilities that accelerate enterprise AI transformation in large banks, or comparable financial institutions
Detecting Hidden Patterns in EEG Waveforms of Schizophrenia Patients using Convolutional Neural Network
Get PDFSchizophrenia is a severe mental disorder that affects 1% of the world’s population and it is characterized by behavioral symptoms such as delusions, hallucinations and disorganized speech. The aim of this research was to develop an artificial intelligence model to detect hidden patterns in electroencephalogram (EEG) waveforms of schizophrenia patients. EEG waveforms of healthy subjects and schizophrenia patients were collected and processed. The data was used to develop a convolutional neural network (CNN) model which can automatically extract features and classify them. CNN does this by comparing the differences between the EEG waveforms of schizophrenia patients and healthy controls. These differences were used to train the classifier to differentiate the schizophrenia patients from the controls. The result of the CNN model showed a test accuracy of 60%, specificity of 55.55% and a precision of 55.55%. This early result shows that the model is promising. The next step will be to improve the accuracy of the model with a larger pool of data and many iterations, which is expected to lead to a better model that can be relied upon for schizophrenia diagnosis. In conclusion, CNN-based models like this one are relatively cheap and will improve the diagnosis of Schizophrenia, especially in low-income economies where the present study has been carried out
Diabetic Retinopathy- Brief Overview
Get PDFDiabetic retinopathy (DR) is a major complication of diabetes, which affects over 90 million people worldwide. Lifetime occurrence of DR is over 90% and 50-60% for Type I diabetes mellitus (T1DM) and T2DM, respectively. Such a high prevalence makes DR a leading cause of blindness in working aged people and a major public health issue in developed countries. In the inaugural issue, this editorial provides a brief overview of the salient features of DR, including risk factors, diagnosis, pathobiology, molecular and cellular mechanisms, and therapeutics. Aspects of DR that are critically important, but not commonly known, will also be discussed
A Multi-class Machine Learning Framework to Predict Ampicillin-Sulbactam Resistance of Acinetobacter baumannii
Get PDFAcinetobacter baumannii is a serious pathogen responsible for many of the hospital-acquired infections. The emergence of multi-drug and pan-drug resistant strains of A. baumannii has been a growing concern. Ampicillin-sulbactam combination has proven to be effective in treatment of several resistant strains. However, strains resistant to ampicillin-sulbactam combination have also emerged necessitating other combination therapy. Rapid and accurate identification of the phenotype of the organism is essential for starting the right treatment. To this end, genome-based approaches have garnered much attention. In this work, we report a multi-class machine-learning based approach to predict the ampicillin-sulbactam resistance phenotype and MIC of Acinetobacter baumannii based on the presence/absence of AMR genes in the genome of strains isolated in the USA region. Our model achieves an accuracy of about 94% indicating that the gene presence/absence itself can capture the resistance phenotype. Further, we show that our model, built based on the USA strains, does not predict reliably the AMR phenotypes of Indian isolates pointing to the need for building machine learning models from region-specific data
Financial Market Efficiency: Equity versus Cryptocurrency before and after Covid-19 Pandemic
Get PDFThe Covid-19 pandemic outbreak may generate differential impacts on global financial markets causing some markets to be more efficient than the others. This paper employs Hurst Exponent as a methodology for measuring financial market efficiency. The literature focused largely on the equity markets such as the stock markets. There is a paucity of studies on the evolving cryptocurrency markets such as Bitcoins. There is also a paucity of studies examining how asset market efficiencies are influenced by the pandemic. We therefore develop testable efficiency market hypotheses for both equities and cryptocurrencies against the backdrop of a large scale global pandemic. We also develop a new theoretical concept in addition to those in the literature for explaining our empirical findings. Our results show that the efficiency level of the cryptocurrency markets is lower than the stock markets. Furthermore, the efficiency level of both the cryptocurrency markets and stock markets decline after the onset of the covid-19 pandemic. The theoretical framework developed in this paper can be used to provide relevant explanations for our empirical results.
 
Optical Spectroscopy and Imaging: An Emerging Method of Cancer Detection
Get PDFOptical Spectroscopy and Imaging: An Emerging Method of Cancer Detectio
Nanoclusters binding energy in diatomic model
Get PDFThere is proposed the general formulation of the diatomic model to calculate nanoclusters binding energy, the key value determining their relative stabilities and, consequently, concentrations in forming processes. The simple special case of practical interest, when all the bond lengths can be assumed to be almost equal each to other is considered in details
Editorial Note
Get PDFI am pleased to introduce the Issue 1 of volume 2 of International Journal of Automation, Artificial Intelligence and Machine Learning (IJAAIML). The journal established recently published its first volume at the end of 2020 and now, according to the scheduled, it is publishing this first issue of the second volume. Although, it is just the beginning, this journal aspires to be a relevant, accessible, integrative and challenging journal. It is important to highlight that all articles of this journal are included in Research Lake journals. That it is, they are completely open access, published under the terms of a Creative Commons license