Environmental education and awareness: the present and future key to the sustainable management of Ramsar convention sites in Kenya

The Ramsar wetland sites are important habitats for biodiversity and provide ecological services to communities that otherwise have no access to water resources. In Kenya, some wetlands are more prominent and are recognized worldwide as tourist hot spots, biodiversity-rich zones and wildlife habitats. However, these wetlands face overexploitation and degradation from surrounding communities. The efforts to halt underlying threats and the projected declines in the size and quality of inland wetlands at local levels are not sufficient. The question guiding this study is as follows: to what extent do a Ramsar designation and formal and informal education support communities and institutional efforts in the protection of inland wetlands? This research was conducted at inland wetland lakes of Naivasha, Nakuru and Bogoria that have been designated as Ramsar sites to examine the extent to which existing education has influenced communities’ efforts in protecting wetlands. Primary data were collected via questionnaire from three study sites. Using both descriptive and inferential statistics, a logistic regression to determine the significance of various predictor variables, including education, for changes in biodiversity as a proxy for wetlands protection outcomes was performed. The results indicated that education, awareness and other key variables that were expected to support wetlands protection had no significant impact on changes in biodiversity. The study concludes that the designation as Ramsar Convention-protected status alone cannot stop the degradation of inland wetlands in an environment where existing formal and informal education has not empowered communities and institutional efforts

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach

Sentiment analysis on film review in Gujarati language using machine learning

Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer

Time Table Generation: Constraint Programming through Random Function Approach

Time table Generation process involves satisfaction of number of constraints. The proposed system usesrandom function approach for generation of time table as well as satisfaction of constraints. In each step of algorithm, constraints are checked and modified constraint status is considered for nextiteration of algorithm

Benefits of Protected Areas to Adjacent Communities: The Case of Maasai Mara National Reserve In Kenya

Kenya has ratified several Multilateral Environmental Agreements (MEAs) such as the Convention on Biological Diversity (CBD) and International Trade in Endangered Species of Flora and Fauna (CITES) and they all emphasize promotion of human well-being - through equitable sharing of benefits accruing from such conservation and protection schemes. This paper is aimed at showing whether the communities living around Kenya's premier conservation area, Maasai Mara National Reserve (MMNR), receive any benefits. A sample of 198 respondents was selected randomly from villages around the MMNR. Data were collected using questionnaires, observations and interviews and analysed by the aid of descriptive and inferential statistics. The results show that majority of the respondents (68.2%) benefit from the Reserve. The largest proportion of those who received benefits (53%) was within 1-2 km range from the Reserve. The benefits mainly included income diversification and access to education. The study recommends equitable sharing of benefits between the Narok County and the surrounding communities to enhance sustainable conservation of wildlife. The neighbouring private and communal lands provide habitats for migratory species whose survival depends on well-being of the local communities. Active engagement of local communities in the conservation of wildlife is, therefore, crucial. This study is part of a larger study on domestication of MEAs in Kenya

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach

Double Field Theory for Generalized λ\lambda-deformation

We embed the geometries of the generalized $\lambda$-deformations into the framework of the Double Field Theory.Comment: 19 pages, v3 Published versio

Classifying Schizophrenia Based on Response to Antipsychotic Medications

Clozapine is the main treatment for patients with significant psychotic symptoms despite adequate trials of non-clozapine antipsychotics. However, 40-70% of patients show suboptimal response to clozapine. We aimed to: (1) examine the relationship between delay in initiating clozapine and treatment outcomes through a literature review; (2) examine clozapine response/non-response trajectories and predictors of long-term non-response through a retrospective chart review; and (3) determine the relationships between glutamatergic neurometabolites and cortical thickness through a cross-sectional imaging study. Delay in initiating clozapine and number of hospitalizations were associated with poor clozapine response. Further, response trajectories ranged from sustained response or non-response to developed response or non-response. Finally, glutamatergic neurometabolite levels in the dorsal anterior cingulate cortex were associated with cortical thinning in the right prefrontal cortex in patients with treatment-resistant schizophrenia, independent of age, sex, treatment, and illness severity. This work advances our understanding of treatment-resistant schizophrenia from clinical and neuropathological perspectives.M.Sc.2020-11-26 00:00:0