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Customer behaviour analysis is essential for optimizing the performance and efficiency of recommender systems. With rapid advancements in deep learning (DL) algorithms, the potential for understanding and predicting customer preferences has significantly improved. Therefore, the given paper proposes an ensemble framework for analysing customer behaviour in order to enhance satisfaction experience and personalized recommendations. The proposed framework consists of dual modules namely purchasing intention (PI) and abandonment analysis (AA) which utilizes amalgamation of three deep learning algorithms namely convolutional neural network (CNN), generative adversarial network (GAN) and long short-term memory recurrent neural network (LSTM-RNN). This ensemble prediction approach provides valuable insights into customer's behaviour and interaction with E-commerce websites. Lastly, the performance of the proposed framework is validated with existing recent studies based on evaluation metrics such as precision, recall, accuracy and F1-score. The framework is intended to provide improved accuracy and generalization capabilities thus enabling better recommendations tailored to individual customer preferences.

An efficient and facile method for the synthesis of substituted imidazoles (10a-10g) has been reported by reacting substituted aldehydes (1–7), benzil (8) and ammonium acetate [9] in presence of reuseable solid acid catalyst viz. Rice Husk Ash.SO3H (RHA.SO3H) at room temperature. The homogeneity of compounds was confirmed by melting point and thin layer chromatography. All synthesized compounds (10a-10g) were characterized by 1H NMR and FTIR spectral techniques. The prepared catalyst has been characterized by SEM, TEM and X-ray diffraction methods. All synthesized compounds were further tested for in vitroherbicidal activity against Raphanus sativusL. seeds. The compounds were also screened for their antifungal activity against Rhizoctonia solaniand Aspergillus nigerby poisoned food technique method. From bio evaluation data, it was found that compounds 10c and 10a were most active against Raphanus sativusL. seeds. Compound 10g was found most active against R. solanifungus at highest concentration. Compound 10f has shown maximum percentage growth inhibition i.e. 71.89 against A. nigerat 200 ​μg/mL concentration.

A green efficient and facile synthesis of pyrazoles is reported viacondensation reaction between substituted aldehydes (1a-1i), malononitrile (2)and 2,4-dinitrophenyl hydrazine (3)in presence of acylated Guar-gum as organocatalystunder solvent-free conditions. The progress of reaction was checked by thin layer chromatography and melting points reported are uncorrected. All synthesized compounds (4a-4i) were characterized by using 1HNMR and FTIR spectral techniques and evaluated for in vitroherbicidal activity against Raphanus sativusL. (Radish seeds). All compounds (4a-4i) were also evaluated for their antifungal activity against Rhizoctonia solaniand Aspergillus nigerby poisoned food techniques method. Antioxidant activity of synthesized compound was also determined. From activity results, it was found that compound 4fwas most active against both Raphanus sativusL. (root) and Raphanus sativusL. (shoot) respectively. Compounds 4eand 4hwere found most active against Rhizoctonia solaniand Aspergillus nigerfungus respectively at highest concentration. Compound 4ehas shown maximum percentage DPPH free radical scavenging activity i.e. 61.47% at 100 ​μg/mL concentration. Less reaction time, excellent yield of products, mild reaction conditions and simple work-up are some merits of present methodology.

Presents a study on the effect of wear on pilling resistance of the commonly used fabric for school children pants. Methodology; Results and discussion; Conclusion.