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.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...     

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.     

Pseudohalogenation of methyl 9-hydroxy-cis-12-and 12-hydroxy-cis-9-octadecenoate

Pseudohalogenation of methyl 9-hydroxy-cis-12-octadecenoate (I) and methyl 12-hydroxy-cis-9-octadecenoate (II) has been carried out with N,N-dibromobenzenesulfonamide (NNDBS). Compounds I and II, on reaction with NNDBS, formed four and three products, respectively. The interesting feature of these reactions was the formation of 1,4-epoxy compounds. The structures of individual compounds were established with the help of elemental and spectral analysis.     

Investigation of the impact of global warming on precipitation pattern of Saudi Arabia

This study has been carried out to forecast the impact of global warming on the precipitation pattern of Saudi Arabia by the end of year 2100. Simulation has been done using EdGCM model software (with available 8×10 resolution) developed at Columbia University on which there have been produced global precipitation maps for the seasonal and annual averages for the last 5 years (2096–2100). For each map, EdGCM grid values surrounding Saudi Arabia are used as input to one of the tools of Eagle point software called surface modelling (SM). SM is a new approach for downscaling global climate model results. SM software modelled out isohyets at 0.2 mm/day interval. The results indicate that the present pattern of precipitation (more in winter and less in summer) is going to change by almost equal occurrence of precipitation in all seasons for double_CO₂ (2CO₂) experiment. The 2CO₂ experiment indicates an increase of about 16.05% over the annual average precipitation across the country.