A Deep Neural Network Model for the Detection and Classification of Emotions from Textual Content

Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory (BiLSMT), for emotion recognition that takes into account five main emotions (Joy, Sadness, Fear, Shame, Guilt). We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information.

Consumers’ Purchase Behavior Preference in E-Commerce Platform Based on Data Mining Algorithm

E-commerce platform can recommend products to users by analyzing consumers’ purchase behavior preference. In the clustering process, the existing methods of purchasing behavior preference analysis are easy to fall into the local optimal problem, which makes the results of preference analysis inaccurate. Therefore, this paper proposes a method of consumer purchasing behavior preference analysis on e-commerce platform based on data mining algorithm. Create e-commerce platform user portrait template with consumer data records, select attribute variables and set value range. This paper uses data mining algorithm to extract the purchase behavior characteristics of user portrait template, takes the characteristics as the clustering analysis object, designs the clustering algorithm of consumer purchase behavior, and grasps the common points of group behavior. On this basis, the model of consumer purchase behavior preference is established to predict and evaluate the behavior preference. The experimental results show that the accuracy rate of this method is 91.74%, the recall rate is 88.67%, and the F1 value is 90.17%, which are higher than the existing methods, and can provide consumers with more satisfactory product information push.

The Impact Of Website Design On Online Customer Buying Satisfaction And Loyalty To E-tailers

The popularity of e-tailers has distorted the retail industry in India. Websites are becoming an important means through which customers get product information and purchase items for their needs. This research paper focuses on four dimensions, i.e. user interface, convenience, personalized recommendations, and perceived security of the website, to assess their impact on online customer satisfaction with and loyalty towards E-tailers. The study questionnaire used established measures. The data was collected from four large cities in India, namely Chennai, Mumbai, Kolkata and Delhi. Analysis of the survey results suggests that perceived website security is the most important dimension for customer loyalty. E-tailers have to ensure adequate security provisions in their websites to build up consumer perceptions of trust and so repeat business loyalty.

New Drug Update: Dostarlimab, Loncastuximab Tesirine, and Aducanumab

Three new drugs are presented: dostarlimab, loncastuximab tesirine, and aducanumab. Product information, clinical trials, and recommendations are provided. Dostarlimab (Jemperli®) is FDA-indicated for the treatment of adult patients with mismatch repair deficient (dMMR - an abnormality that affects DNA repair) recurrent or advanced endometrial cancer (EC). Loncastuximab tesirine (Zynlonta®) is FDA-indicated for the treatment of adult patients with relapsed or refractory large B-cell lymphoma after two or more lines of systemic therapy, including diffuse large B-cell lymphoma (DLBCL) not otherwise specified, DLBCL arising from low grade lymphoma, and high-grade B-cell lymphoma. Aducanumab (Aduhelm®) is FDA-indicated for the treatment of Alzheimer’s disease. It is an amyloid beta-directed antibody developed by Biogen of Cambridge, Massachusetts. This indication was approved on June 7, 2021, under the accelerated approval based on the reduction in amyloid beta plaques in patients treated with the drug.

The effect of implementing information systems on MS Glow

Ms Glow is a company engaged in the beauty sector. Ms Glow is a local brand that presents a series of skincare, especially for Indonesian women, which was founded in 2013. Technology is growing very fast as is online sales. This Ms Glow Information System was designed to serve as a promotional medium for Ms Glow and to simplify the online buying and selling transaction process. The design of promotional media also considers the factors that can affect the target market and target audience, so that the desires of potential customers can be identified. The website created can display complete and clear information and contain transaction reports, which sometimes errors occur when processing data. Ms Glow's Information System is a website that makes it easy for shop owners and consumers to make sales transactions, purchase goods and report data. Using easy-to-read fonts, attractive photo images, and choosing the right color. This system is created using the PHP programming language with the MySQL database for data storage. The result of this system is that it can process product information, such as: managing user data, category data, goods data, and the process of purchasing goods transactions. Through this website, it is hoped that Ms Glow as previously mentioned can be implemented and improved

An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

Modular architecture is very conducive to the development, maintenance, and upgrading of electromechanical products. In the initial stage of module division, the design structure matrix (DSM) is a crucial measure to concisely express the component relationship of electromechanical products through the visual symmetrical structure. However, product structure modeling, as a very important activity, was mostly carried out manually by engineers relying on experience in previous studies, which was inefficient and difficult to ensure the consistency of the model. To overcome these problems, an integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm (GA) is presented. First, the product information extraction algorithm is designed based on the automatic programming structure provided by commercial CAD software, to obtain the assembly, degrees of freedom, and material information needed for modeling. Secondly, based on the evaluation criteria of product component correlation strength, the structural correlation DSM and material correlation DSM of components are established, respectively, and the comprehensive correlation DSM of products is obtained through weighting processing. Finally, the improved GA and the modularity evaluation index Q are used to complete the product module division and obtain the optimal modular granularity. Based on a model in published literature and a bicycle model, comparative studies are carried out to verify the effectiveness and practicality of the proposed method.

Optimal Decision-Making Model of Agricultural Product Information Based on Three-Way Decision Theory

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information.

Life Cycle Assessment of Industrial Building Construction and Recovery Potential. Case Studies in Seville

In Spain, most businesses are medium to small size enterprises, representing 90% of the total, but there is a lack of studies of the types of building this sector uses. The main objective of this paper is to present a method for the evaluation of small industrial construction projects to facilitate the introduction of eco-efficient solutions. For this, it is necessary to identify the most representative buildings and the aspects of these which have the most environmental impact. A methodology in place for the evaluation of dwelling construction is adapted, for the first time, to evaluate industrial buildings. The construction solutions characterized are those traditionally used in the sector, as identified through 87 surveys. A standardized classification of work units is proposed to enable the use of environmental product information, such as eco-labels and/or EPD, and LCA databases. The carbon footprint (CF) and water footprint (WF) are the indicators selected because of their straightforward message. Finally, a comparative analysis is performed showing the high recycling potential of concrete and cement which, along with metals and aggregates, control the impact in terms of CF. With respect to the WF indicator, plastic substitute aggregates are among the materials with the greatest impact.