No abstract

Abstract-Semantic-based image retrieval has been one of the most challenging problems in recent years. Although so many solutions are provided for filling the so-called gap between the content based image retrieval (CBIR) and what human beings expect from the retrieval task; none of them yields satisfactory results and the problem is still open for further research. In this paper, type-2 fuzzy logic (T2FL) framework is considered to alleviate two problems in traditional CBIR systems, including the semantic gap and the perception subjectivity. Employing T2FL has the potential to overcome the limitations of type-1 fuzzy logic and produce a new generation of fuzzy controllers with improved performance for many CBIR applications that require handling high levels of uncertainty. Thus, our contributions in this study are threefold.(1) The proposed system maps low-level visual statistical features to high-level semantic concepts; enabling to retrieve and browse image collections by their high-level semantic concepts. (2) Type2 fuzzy logic has been used to fuse (combine) extracted features as well as to deal with the ambiguity of human judgment of image similarity. (3) The system models the human perception subjectivity with the ability to handle high levels of uncertainties appropriately. A comparative study with the state-of-the-art type-1 fuzzy based image retrieval approaches reveals the effectiveness of the proposed system. Index Terms-Type-2 fuzzy logic, semantic-based image retrieval, soft computing, image processing.

In this work, the Upflow Anaerobic Sludge Blanket “UASB” reactor treated effluent wastewater to investigate the process performance on a pilot plant scale. Municipal wastewater at high and medium strength with different organic load rate OLR (0.6-9) kg COD m-3day-1 with the flow of 20 l/h, up-flow velocity 0.4 m/h, hydraulic retention time HRT 9 h at a temperature of (20-30 ºC) was evaluated. The wastewater concentration, including TSS, COD was measured, and the removal efficiencies of chemical oxygen demand (COD) and total suspended solid TSS were calculated and summarized as 45-85% and 70-75%, respectively, depending on organic load rate OLR. Effluent volatile fatty acids VFA was measured, and the results were in the range between 12-90 mg/L depending on OLR with a slight change in pH (8.3-8.4), which means the conversion of COD to methane and increase ammonia concentration.

In this work a practical comparison has been done between the conventional coagulation and flocculation method and Ballast flocculation as a primary settling procedure that was used to treat sewage water in Al-Zaafraniya apartment complex. The results show that the conventional method using alum as coagulant 75 mg/L, gives removal efficiencies of Total suspended solid TSS 50-60%, BOD 30-40% and total phosphorus TP 40% at total time of 60 minutes. While the Ballast flocculation method using alum 75 mg/L , polyelectrolyte 1.5 mg/L and sand 10 mg/l (70µm) gives removal efficiencies of TSS 50-70%, BOD 30-40% and TP 68% at total time less than 22 minutes.From the above, a conclusion can be noticed that the Ballast flocculation method means reduction in equipment size and footprint area more over efficiency in total phosphorus removal.

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback.