Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

Brick is one of the most important masonry unit as a building material due to its properties. Many attempts have been made to incorporate wastes into the production of bricks. In this paper we experimentally describe the recycling of the waste products like STP Sludge and Fly ash by incorporating them into bricks. It is a practical solution for problems like cost expenditure on waste management and its effect on environment. The STP sludge and Fly ash is extremely close to brick clay in chemical composition so, it could be a potential substitute for clay bricks. The sewage treatment process generates a sludge that must be disposed-off in an environmentally sound manner. The sludge generated in most of the treatment systems around the world is discharged into the nearest watercourse. Among all disposal options, the use of sludge in producing constructional elements is considered to be the most economic and environmentally sound option. This paper reviews the recycle of sewage treatment plant sludge and Fly ash against fired clay bricks. Bricks so formed have adequate crushing strength, hardness and water absorption.

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Pollutants in amphibian conditions will in general change the network arrangement and exercises of biofilm cells. The making of excessive reactive oxygen species (ROS) which is caused due to the stress induced out of these pollutants play a major role in debasement of pollutants in oceanic conditions. This smaller than expected audit is pointed toward anticipating the proficiency of the utilisation of regular biofilm in water treatment coming about because of mining exercises. Additionally, it sums up the idea of mining by briefly expressing the stages engaged with mining and how the toxins delivered during mining can make harmful impacts on amphibian vegetation. Discussion on how the advancement of biodiversity is impacted by the ecological issues resulting from activities of mining was done. Microalgae assume a significant part in the alleviation of the effect of contamination coming about because of mining exercises. The cycle of toxins expulsion in freshwater by miniature green growth biofilm is by debasement and biosorption. Biosorption of hefty metals in mining wastewater was caused because of the extracellular polymeric substances (EPS). The kinetic model should be understood properly which explains the biosorption of hefty metals through physical and chemical reactions.

The scarcity of dependable product descriptions and limited emotion unmasking capabilities of user-ratings compromise the accuracy of content-based filtering (CBF) systems. This work puts forward a sentiment-enhanced content-based recommender system (SEC-Rec). The model has four modules, namely key feature extraction module, feature sentiment analysis module, recommendation module, and rating prediction module. Key feature extraction module uses hybrid of RAKE and TextRank to uncover key product features. The authors propose a hybridized model HSVADER (Hybrid SVM and VADER) for feature sentiment evaluation. The recommendation module combines sentiment and similarity for robust product ranking strategy. The practical benefits of SEC-Rec are demonstrated using Amazon Camera dataset, and the results are compared to the state of the art. The rating prediction module uses key feature sentiment score to estimate the overall user-rating resolving the multi-criteria decision-making issue. The RMSE value obtained ascertains the effectiveness of the approach compared to recent models.