CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis

Recognizing emotional state of human using brain signal is an active research domain with several open challenges. In this research, we propose a signal spectrogram image based CNN-XGBoost fusion method for recognising three dimensions of emotion, namely arousal (calm or excitement), valence (positive or negative feeling) and dominance (without control or empowered). We used a benchmark dataset called DREAMER where the EEG signals were collected from multiple stimulus along with self-evaluation ratings. In our proposed method, we first calculate the Short-Time Fourier Transform (STFT) of the EEG signals and convert them into RGB images to obtain the spectrograms. Then we use a two dimensional Convolutional Neural Network (CNN) in order to train the model on the spectrogram images and retrieve the features from the trained layer of the CNN using a dense layer of the neural network. We apply Extreme Gradient Boosting (XGBoost) classifier on extracted CNN features to classify the signals into arousal, valence and dominance of human emotion. We compare our results with the feature fusion-based state-of-the-art approaches of emotion recognition. To do this, we applied various feature extraction techniques on the signals which include Fast Fourier Transformation, Discrete Cosine Transformation, Poincare, Power Spectral Density, Hjorth parameters and some statistical features. Additionally, we use Chi-square and Recursive Feature Elimination techniques to select the discriminative features. We form the feature vectors by applying feature level fusion, and apply Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) classifiers on the fused features to classify different emotion levels. The performance study shows that the proposed spectrogram image based CNN-XGBoost fusion method outperforms the feature fusion-based SVM and XGBoost methods. The proposed method obtained the accuracy of 99.712% for arousal, 99.770% for valence and 99.770% for dominance in human emotion detection.publishedVersio

An AI-Based Framework for Translating American Sign Language to English and Vice Versa

Abstract: In this paper, we propose a framework to convert American Sign Language (ASL) to English and English to ASL. Within this framework, we use a deep learning model along with the rolling average prediction that captures image frames from videos and classifies the signs from the image frames. The classified frames are then used to construct ASL words and sentences to support people with hearing impairments. We also use the same deep learning model to capture signs from the people with deaf symptoms and convert them into ASL words and English sentences. Based on this framework, we developed a web-based tool to use in real-life application and we also present the tool as a proof of concept. With the evaluation, we found that the deep learning model converts the image signs into ASL words and sentences with high accuracy. The tool was also found to be very useful for people with hearing impairment and deaf symptoms. The main contribution of this work is the design of a system to convert ASL to English and vice versa

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively

Environmental study on water quality of Mayur River with reference to suitability for irrigation

ABSTRACT The farmers residing in the western fringe of Khulna city in Bangladesh use the sewage-fed water of the Mayur River for irrigation as good quality surface water is not available as well as higher cost in groundwater irrigation. The present study was undertaken to evaluate the suitability of this river water for irrigation during the hot summer months (March to May) since this period is more sensitive to crop agriculture in the study area. A total of 30 water samples, 10 in each month from 10 stations, were collected and analyzed for pH, EC, TDS, major cations (Na, K, Ca, Mg) and anions (Cl, HCO 3 , NO 3 , PO 4 , SO 4 ), Chemical data were used for calculation of SAR, Na%, RSC, PI, KI and MR for better understanding the suitability of river water for irrigation use. Wilcox diagram and USSL diagram were also adopted in the present study to verify the suitability of river water quality for irrigation. The results revealed that water of the Mayur River was alkaline in nature like major world rivers. Sodium was the most dominant cation throughout the sampling period, whereas sulfate was the most dominat anion in March and May, and chloride in April. The river water was found to be safe for irrigation with respect to pH and PI. However, some usual and calculated parameters like EC, TDS, hardness, alkalinity, chloride, sulfate, nitrate, sodium, Na%, SAR, KI, MR and Ca/Mg restricted the Mayr River water for use in irrigation. The RSC values indicated the water to be safe during April and May and permissible to severe in March. The USSL and Wilcox diagrams indicated high salinity in the water with high sodium being unsuitable for irrigation. The findings call for an immediate management plan to protect this invaluable resource

Willingness to pay for improved drinking water in southwest coastal Bangladesh

Households in the rural areas of Southwest coastal Bangladesh mainly depend on unreliable sources of drinking water. This study assessed the willingness to pay (WTP) for improved drinking water in a rural area of the Southwest coastal Bangladesh, using contingent valuation survey data of 215 households. The samples for the face-to-face interview were selected by purposive random sampling from Chila union of Mongla sub-district under Bagerhat district. The mean WTP for improved drinking water was estimated to be BDT 193 (US$ 2.47) per month (3% of the monthly income of the households). Results also indicate that educated respondents and households with higher income are willing to pay more for improved water supply. Moreover, the expenditure of the households for buying water and for medicine for waterborne diseases has a significant positive impact on the WTP. The results of this study can be useful for decision-makers to promote improved drinking water supply in Southwest coastal Bangladesh

Land use change and its effect on biodiversity in Chiang Rai province of Thailand

The Chiang Rai province of Thailand has experienced rapid deforestation and consequent land use change in recent years. This research carried out in Chiang Rai province simulated future land uses under three management scenarios and assessed their effect on biodiversity. The Dyna-CLUE model was used to simulate land uses of 2029 according to the management scenarios, and their effect on biodiversity was analyzed by the GLOBIO3 model. About 4% of total area of the province was deforested within 2002–2009 and has a possibility to lose more, that is, 7% by 2029. If this rate of deforestation continues, then biodiversity will be affected as shown by reduced ‘mean species abundance’ of 0.45 at present to 0.38 by 2029, whereby highly threatened area can be increased up to 15% of the total land area. Hence, protecting locations with higher biodiversity value can be efficient in conserving biodiversity

Water quality of small-scale desalination plants in southwest coastal Bangladesh

Southwest coastal Bangladesh has an acute scarcity of safe drinking water. Both the government and non-government organizations are now promoting reverse osmosis based small scale desalination plants (SSDPs) to ensure safe drinking water. The aim of this study was to assess the physico-chemical and bacteriological quality of the desalination plants (DPs) installed in southwest coastal Bangladesh. Water samples were collected from the inlet and outlet of 10 DPs. The product water mostly complied with water quality standards. High levels of total dissolved solids (TDS) and electrical conductivity (EC) in feed water were reduced significantly after the treatment, although 10% and 20% of the product water samples respectively did not comply with the WHO drinking water standards for those parameters. Compliance of product water with the WHO and Bangladesh drinking water standards for chloride, bicarbonate and sodium were found in respectively 80%, 90% and 70% of the samples, although their concentrations in all the feed water samples were higher than both of the standards. About one-third of the DPs did not meet the drinking water standard for sodium, which may be an important health concern for the people consuming this water. Apart from one of the DPs, all of them complied with the standard for faecal coliform and Escherichia coli. Results suggest that proper maintenance of the SSDPs is necessary to ensure safe drinking water for the coastal population of southwest Bangladesh

Potential ecological risk of metal pollution in lead smelter-contaminated agricultural soils in Khulna, Bangladesh

Lead smelters are important source of metal pollution. This study assessed ecological risks of three heavy metals (Pb, As, and Zn) in agricultural soils surrounding five Pb smelters from Khulna district in Bangladesh. A total of 81 surface soil samples collected within 500-m radius of the smelters were analyzed using an atomic absorption spectrophotometer (AAS). Concentrations of Pb, As, and Zn ranged within 6–3902, 1.8–9.6, and 45.4–563\ua0mg/kg, respectively. About half of the Pb samples (~ 51%) exceeded soil quality standard target value (85\ua0mg/kg), and the concentrations gradually decreased with horizontal distance from the smelter. The value of pollution index (PI) measured for Pb, As, and Zn varied respectively in the range of 0–195, 0.6–3.2, and 0.67–8.28, with mean values of 11.7, 1.9, and 3.92. The value of integrated pollution index (IPI) calculated for these metals remained between 0.58 and 66.2 with a mean of 5.7, and that designates ~ 96% of the sampled soils as moderately or highly contaminated. Potential ecological risk (PER) calculated for the metals indicate that all the samples were within low to moderate risk, and the descending order of PER of the metals was Pb > As > Zn

Trace metals accumulation in soil irrigated with polluted water and assessment of human health risk from vegetable consumption in Bangladesh

Trace metals accumulation in soil irrigated with polluted water and human health risk from vegetable consumption was assessed based on the data available in the literature on metals pollution of water, soil, sediment and vegetables from the cites of Bangladesh. The quantitative data on metal concentrations, their contamination levels and their pollution sources have not been systematically gathered and studied so far. The data on metal concentrations, sources, contamination levels, sample collection and analytical tools used were collected, compared and discussed. The USEPA-recommended method for health risk assessment was used to estimate human risk from vegetable consumption. Concentrations of metals in water were highly variable, and the mean concentrations of Cd, Cr, Cu and As in water were found to be higher than the FAO irrigation water quality standard. In most cases, mean concentrations of metals in soil were higher than the Bangladesh background value. Based on geoaccumulation index (I) values, soils of Dhaka city are considered as highly contaminated. The I shows Cd, As, Cu, Ni, Pb and Cr contamination of agricultural soils and sediments of the cities all over the Bangladesh. Polluted water irrigation and agrochemicals are identified as dominant sources of metals in agricultural soils. Vegetable contamination by metals poses both non-carcinogenic and carcinogenic risks to the public. Based on the results of the pollution and health risk assessments, Cd, As, Cr, Cu, Pb and Ni are identified as the priority control metals and the Dhaka city is recommended as the priority control city. This study provides quantitative evidence demonstrating the critical need for strengthened wastewater discharge regulations in order to protect residents from heavy metal discharges into the environment