Applied Science and Engineering Journal for Advanced Research <p>Applied Science and Engineering Journal for Advanced Research is a bi-monthly, online, double blind peer reviewed open access international journal. This journal publish research papers from all the discipline of applied sciences, Medicine and engineering related subjects. Published papers are freely accessible online in full-text and with a permanent link to the journal's website.</p> <p><strong>JOURNAL PARTICULARS</strong></p> <p><strong>Title:</strong> Applied Science and Engineering Journal for Advanced Research<br /><strong>Frequency:</strong> Bimonthly (6 issue per year)<br /><strong>ISSN (Online):</strong> <a href="" target="_blank" rel="noopener">2583-2468</a><br /><strong>Publisher:</strong> Singh Publication, Lucknow, India. (Registered under the Ministry of MSME, Government of India. Registration number: “UDYAM-UP-50-0033370”)<br /><strong>Chief Editor:</strong> Dr. Ashutosh Kumar Bhatt<br /><strong>Copyright:</strong> Author<br /><strong>License:</strong> Creative Commons Attribution 4.0 International License<br /><strong>Starting Year:</strong> 2022<br /><strong>Subject:</strong> Applied Science and Engineering <br /><strong>Language:</strong> English<br /><strong>Publication Format:</strong> Online<br /><strong>Contact Number:</strong> +91-9555841008<br /><strong>Email Id:</strong><br /><strong>Journal Website:</strong> <a href=""></a><br /><strong>Publisher Website:</strong> <a href="" target="_blank" rel="noopener"></a><br /><strong>Address:</strong> 78/77, New Ganesh Ganj, Opp. Rajdhani Hotel, Aminabad Road, Lucknow-226018, Uttar Pradesh, India.</p> en-US (Dr. Ashutosh Kumar Bhatt) (Dr. Amarjeet Singh) Sat, 06 Jul 2024 07:38:39 +0000 OJS 60 Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models <p>This paper explores the application and advantages of large-scale AI models in logistics and supply chains. Traditional enterprises need help with the timely detection of anomalies in the supply chain. At the same time, AI algorithms can quickly identify abnormal patterns in the data and issue alerts, helping enterprises adjust real-time strategies to ensure the supply chain's stable operation. AI also reduces inventory costs and economic losses by predicting changes in market demand and optimizing inventory management. In addition, AI models perform well in intelligent scheduling and route planning, providing optimized solutions based on factors such as traffic flow, road conditions, and weather forecasts to improve transportation efficiency and accuracy. The article details the system architecture and functional modules designed to help enterprises meet the transformation challenges of the digital age.</p> Zeqiu Xu, Lingfeng Guo, Shuwen Zhou, Runze Song, Kaiyi Niu Copyright (c) 2024 Zeqiu Xu, Lingfeng Guo, Shuwen Zhou, Runze Song, Kaiyi Niu Sat, 06 Jul 2024 00:00:00 +0000 LLM for Sentiment Analysis in E-commerce: A Deep Dive into Customer Feedback <div>With the rise of online shopping becoming an integral part of daily life, it has brought unparalleled convenience, allowing us to purchase anything from daily necessities to luxury items with ease. On platforms like Amazon[1], customer feedback mechanisms play a crucial role in shaping user behavior and business practices. These mechanisms include the Star Rate (1-5) and detailed reviews. The star rating provides a quick and intuitive way for customers to score a product, while reviews offer comprehensive descriptions and shopping experiences. These feedback systems influence other customers' purchasing decisions and provide valuable insights for businesses to improve their products.</div> <div>In our study, we explore the impact of time on user behavior by combining three datasets for analysis. We observe a macro trend of increasing online shopping activity and customer satisfaction over time, with a growing tendency for customers to leave feedback. From a micro perspective, we conduct time series analysis and establish that customer star ratings vary over time, forming a column relationship, which we model using the ARIMA[2] technique to predict future trends.</div> <div>Furthermore, we investigate the influence of reviews on customer responses, finding that negative reviews spread rapidly and widely through social networks, akin to a viral phenomenon. Utilizing the SIR virus model as a text-based network propagation model, we demonstrate the significant impact of negative reviews on consumption patterns.[3]</div> <div>Lastly, we employ advanced deep learning models such as BERT, LLM, and GPT for natural language processing (NLP) to analyze the sentiment in customer reviews. By leveraging word vectors through classification functions, we distinguish between pessimistic and optimistic emotions. Our findings reveal a higher-order functional relationship[4] between star ratings and these emotions, providing deeper insights into customer sentiment and product perception.</div> Zeyue Wang, Yue Zhu, Shuyao He, Hao Yan, Ziyi Zhu Copyright (c) 2024 Zeyue Wang, Yue Zhu, Shuyao He, Hao Yan, Ziyi Zhu Fri, 12 Jul 2024 00:00:00 +0000