Application of computational intelligence methods for the automated identification of paper-ink samples based on LIBS

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate

ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System

Deep learning has emerged to be efficient Artificial Intelligence (AI) phenomena to solve problems in healthcare industry. Particularly Convolutional Neural Network (CNN) models have attracted researchers due to their efficiency in medical image analysis. According to World Health Organization (WHO), rapidly developing cerebral malfunction, brain stroke, is the second leading cause of death across the globe. Brain MRI scans, when analysed quantitatively, play vital role in diagnosis and treatment of stroke. There are many existing methods built on deep learning for stroke diagnosis. However, an automatic, reliable and faster method that not only helps in stroke diagnosis but also demarcate affected regions as part of Clinical Decision Support System (CDSS) is much desired. Towards this objective, we proposed an Automated Deep Learning based Brain Stroke Detection Framework (ADL-BSDF). It does not rely on expertise of healthcare professional in diagnosis and know the extent of damage enabling physician to make quick decisions. The framework is realized by two algorithms proposed. The first algorithm known as CNN-based Deep Learning for Brain Stroke Detection (CNNDL-BSD) focuses on accurate detection of stroke. The second algorithm, Deep Auto encoder for Stroke Severity Detection (DA-SSD), focuses on revealing extent of damage or severity of the stroke. The framework is evaluated against state of the art deep learning models such as EfficientNet, ResNet50 and VGG16

Validation of Fast Spectrochemical Screening Methods for the Identification of Counterfeit Pharmaceutical Packaging

Counterfeit pharmaceuticals are an actively developing health and economic threat worldwide. Particularly prevalent are counterfeit pharmaceuticals distributed in emerging nations and through internet pharmacies or e-pharmacies. Although technology has been developed that discourages anti-counterfeiting practices (such as optically variable devices, invisible ink, and track-and-trace technology), it remains somewhat novel and expensive to implement on a widespread scale. In this study, Laser Induced Breakdown Spectroscopy (LIBS) and Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) were proposed as fast and non-invasive tools for the identification of counterfeit pharmaceutical packages. The main objective of this research was to develop and evaluate the capabilities of LIBS and ATR-FTIR to determine chemical differences between counterfeit and authentic pharmaceutical packaging samples. LIBS and ATR-FTIR possess several characteristics that render them suitable for rapid on-site detection. They produce analytical results in less than one minute per sample, with high sensitivity and selectivity, limited sample preparation, and minimal destructivity. The methods were evaluated through the analysis of a dataset of 166 packages (112 counterfeits and 54 authentic sources). The dataset was divided into two main subsets. The first subset was evaluated to identify the informative value of LIBS for fast screening of black barcodes and the carton substrate (100 counterfeit and 35 authentic). The multi-color inks and paper of the second subset was investigated for variation of chemical profiles within and between sources, and the method’s capabilities to distinguish between counterfeits (112) and authentic samples (12). One hundred and twelve counterfeit pharmaceutical cartons were printed from five different sources, mimicking six authentic counterparts. The authentic subset consisted of twelve secondary packages of six common medical products, including packages from the same and different manufacturing lots. The selected products consisted of vasodilators, antivirals, steroids, and other commonly counterfeited pharmaceuticals. Intra-source variation of the counterfeit subset was investigated; it was determined to be sufficiently lower than inter-source variation. False exclusion rates were calculated to be less than 20% for samples originating from the same source (e.g., same package, intra-lots, replicate printouts). Using LIBS, a two-class classification system was used for the combined black barcode ink and paperboard carton spectra (n = 135, 100 counterfeit, 35 authentic packages). As black barcode ink is very common on pharmaceutical packaging, this system was used as a general screening technique to quickly identify a sample as authentic or counterfeit, regardless of counterfeit printing source. In general, the correct classification rates for this set were over 92%. The classification models were established using six machine learning methods: Random Forest, Naïve Bayes, Neural Networks, k-Nearest Neighbors, Quadratic Discriminant Analysis, and Linear Discriminant Analysis. A random split of 60% and 40% of the dataset was applied for training and testing of the classifier algorithms. Principal Component Analysis (PCA) was utilized on the LIBS and ATR-FTIR data for variable reduction purposes. The principal components for each ink type were combined prior to classification. Also, a six-class system was also used to classify the dataset using LIBS, ATR-FTIR, and combined data from both techniques (n = 124, 112 counterfeit, 12 authentic packages). The machine learning methods classified the samples as belonging to one of five counterfeit printing sources or their corresponding authentic counterpart. Seven ink colors (red, blue, yellow, green, brown, pink, black) were analyzed; additionally, in ATR-FTIR, the paperboard substrate was also analyzed. In most comparisons, LIBS had a successful classification rate of over 70% and ATR-FTIR had a successful classification rate of over 85%. When the data from both techniques were combined, the discrimination power of the system increased to 93% correct classification. Although LIBS and ATR-FTIR had a low misclassification rate when used in isolation, the misclassification rate could be reduced even further through data combination. The results of this study are encouraging for the inclusion of LIBS and ATR-FTIR as a screening method for the detection of counterfeit pharmaceutical packaging. The utilization of combined data to discover chemical signatures addresses an urgent need in the investigation of counterfeit pharmaceuticals. Also, the classification of counterfeit samples into their specific counterfeit source may benefit investigators as they make determinations in the counterfeit pharmaceutical packaging supply chain. This study is anticipated to offer relevant tools to both government and pharmaceutical industry in the detection and fight against counterfeit pharmaceuticals