Application of GA-Optimized ANNs to Predict the Water Content,CO2 and H2S Absorption Capacity of Diethanolamine (DEA) in Khangiran Gas Sweetening Plant

Theoretical Foundations of Chemical Engineering - In this work, with the aim of accurate prediction of water content, H2S and CO2 absorption capacity of diethanolamine (DEA) solvent in Khangiran...     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

Quantitative preparation of long chain thioethers from an oxo-trans-2-enoic ester and a 9,12-dioxo-trans-10-enoic acid

Branched-chain thioethers have been prepared from methyl 4-oxo-trans-2-hexadecenoate and 9,12-dioxo-trans-10-octadecenoic acid. The reagents involved in these preparations were mercaptoacetic and mercaptopropionic acids. The yields of these thioethers are almost quantitative.     

Synthesis of tetrazole from α, β—Unsaturated carbonyl fatty acid

Methyl 4-oxo-trans-2-octadecenoate (II), when treated with excess hydrazoic acid in the presence of BF₃-etherate, produced 66% methyl 5-aza-nonadec-trans-2-enoate (4,5-d)-tetrazole (III), 10% methyl 5-aza-nonadec-4-oxo-trans-2-enoate (IV) and 7% pentadecamide (V). Individual products were characterized by spectral and elemental methods.     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

Knowledge transfer in SVM and neural networks

The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.     

A correlation-based ant miner for classification rule discovery

In recent years, a few sequential covering algorithms for classification rule discovery based on the ant colony optimization meta-heuristic (ACO) have been proposed. This paper proposes a new ACO-based classification algorithm called AntMiner-C. Its main feature is a heuristic function based on the correlation among the attributes. Other highlights include the manner in which class labels are assigned to the rules prior to their discovery, a strategy for dynamically stopping the addition of terms in a rule’s antecedent part, and a strategy for pruning redundant rules from the rule set. We study the performance of our proposed approach for twelve commonly used data sets and compare it with the original AntMiner algorithm, decision tree builder C4.5, Ripper, logistic regression technique, and a SVM. Experimental results show that the accuracy rate obtained by AntMiner-C is better than that of the compared algorithms. However, the average number of rules and average terms per rule are higher.     

Experimental Study of RF-excited Diffusion Cooled Off-axis Unstable Resonator with High Frequency Modulation in a Waveguide CO2 Laser

The experimental study of the laser beam parameters of the pulse repetitive RF-excited diffusion cooled waveguide CO2 laser are presented. The measurements are carried out for the pumping pulse duration of 100 μs and pulse repetitive rates 5 - 14 kHz. The average power density delivered to the active medium is 76 W/cm^3. Three types of the pulses, namely the square, the sine and the triangular ones have been applied at the input as pumping pulses and their effects on the output power and the delay time have been studied. The output power of the radiation versus input power, pressure of the laser gas mixture and modulation frequency has been investigated. The results indicate that the output peak power for the three types of pulses increases with increase of the pressure of the laser gas mixture and with the input power where as it decreases with the repetition frequency. The delay time of the output pulse decreases with the increase of the repetition frequency and input power, where as it increases with the increase of the pressure of the laser gas mixture. The behavior of the output power and the delay time with duty cycle for square pulse has also been investigated.