The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.
Identification and classification of biometrics are important research areas in the field of image processing and pattern recognition. Biometrics are the measurement and statistical analysis of physiological and behavioral characteristics of humans. A wide variety of biometric modalities are available, with unimodal biometrics suffering from several factors. The proposed research is novel because it uses a single image of a hand in order to extract a variety of unique characteristics, like hand shape and the palmprint associated with individual hands. Moreover, it obtains higher accuracy with minimum effort. We have chosen the multimodal biometrics, i.e., palmprint and hand shape, from three datasets, i.e., PolyU Palmprint Database, GPDS Hand Database, and the Bosphorus Hand Database, for a total of 1,072 images. There are 302 textural features found in the palmprint images, and 12 geometrical features are extracted from the hand images. Classification models include Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (IBk), Decision Tree, Random Tree, Random Forest, and Bagging. The train and test method is used to evaluate the performance of different classifiers. It is observed that Naïve Bayes, SVM, IBk, and Random Tree models result in classification accuracy of 99.44 % with palmprint images using the 302 textural features over the combined dataset. After feature reduction, similar accuracy is achieved with the top ten, and even with the top five, features. For geometrical features, an accuracy of 99.81 % is achieved with the hand images using Naïve Bayes, SVM, IBk, and Random Tree.
In this paper, we have proposed a novel approach for the prevention of the Internet of Things (IoT) from fake devices and highlighted privacy issues by using third party Application Program Interface (RestAPI) in Web of Things (WoT). For the ease of life, the usage of IoT devices, sensors, and Radio-Frequency Identifications (RFIDs) increased rapidly. Such as in transport for monitoring vehicles, taxi services, healthcare for patient's health condition monitoring, smart cars, smart grids, and smart homes, etc. Due to this for financial gain attackers are targeting these networks or protocol and adversaries are trying to damage the reputation of the organization or to steal intellectual property. From the last two decades or more, the injection vulnerabilities are more threatening security risks for the web application still exists. The new security challenges occur for the security professional or security researchers in the form of IoT or WoT (Web of Things) communication protocols implementation. These protocol Message Queuing Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), WebSockets, and RestAPI have a different type of security issues. Respectively insertion of fake devices, authentication is not implemented in WebSocket connections, and user's privacy can be leaked with the use of RestAPI without its validation. We have developed a program in Personal Home Pages (PHP) for the detection of new devices in the IoT network. With this, the user's privacy and data will be protected along with some critical security issues of WoT underlying protocols.
The nondestructive analysis of a solid pharmaceutical product (SPP) is essential to evaluating the quality without destroying the product. This analysis may be performed using various signal processing techniques on multispectral data. Based on this analysis, the SPPs may be classified as defective or nondefective. In this research, we have used multispectral data and applied wavelet transformations in conjunction with various machine learning techniques for the classification. The SPPs (categorized as defective) are exposed to three different environmental factors (humidity, temperature, and moisture) over different time periods, and the variations in multispectral data are analyzed to judge the effects of these factors on the classification of the SPPs. The results show that the spectra extracted from only the ultraviolet (UV) wavelength range are more suitable for the classification of defective and nondefective SPPs. Furthermore, results also describe that the K-nearest neighbors classifier or ensemble of classifiers is a more appropriate classifier.
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