T. Musora, Z. Chazuka, A. Jaison, J. Mapurisa, J. Kamusha
{"title":"Sales Forecasting of Perishable Products: A Case Study of a Perishable Orange Drink","authors":"T. Musora, Z. Chazuka, A. Jaison, J. Mapurisa, J. Kamusha","doi":"10.5121/csit.2023.130408","DOIUrl":"https://doi.org/10.5121/csit.2023.130408","url":null,"abstract":"The primary goal of any organization involved in trading business is to maximize profits while keeping costs to a bare minimum. Sales forecasting is an inexpensive way to achieve the aforementioned goal. Sales forecasting frequently leads to improved customer service, lower product returns, lower deadstock, and efficient production planning. Because of short shelf life of food products and importance of product quality, which is of concern to human health, successful sales forecasting systems are critical for the food industry. The ARIMA model is used to forecast sales of a perishable orange drink in this paper. The methodology is applied successfully. ARIMA (0,1,1)(0,1,1)12 was concluded as the appropriate model. Model diagnostics were done; results showed that no model assumption was violated. Fitted values were regressed against observed values. A very strong linear relationship was evident with an R 2 value of over 90% which is very plausible.","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130865249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Knowledge-Enriched Moral Understanding upon Continual Pre-training","authors":"Jing Qian, Yong Yue, Katie Atkinson, Gangmin Li","doi":"10.5121/csit.2023.130414","DOIUrl":"https://doi.org/10.5121/csit.2023.130414","url":null,"abstract":"The aim of moral understanding is to comprehend the abstract concepts that hide in a story by seeing through concrete events and vivid characters. To be specific, the story is highly summarized in one sentence without covering any characters in the original story, which requires the machine to behave more intelligently with the abilities of moral perception and commonsense reasoning. The paradigm of “pre-training + fine-tuning” is generally accepted for applying neural language models. In this paper, we suggest adding an intermediate stage to build the flow of “pre-training + continual pre-training + finetuning”. Continual pre-training refers to further training on task-relevant or domainspecific corpora with the aim of bridging the data distribution gap between pre-training and fine-tuning. Experiments are basing on a new moral story dataset, STORAL-ZH, that composes of 4,209 Chinese story-moral pairs. We collect a moral corpus about Confucius theory to enrich the T5 model with moral knowledge. Furthermore, we leverage a Chinese commonsense knowledge graph to enhance the model with commonsense knowledge. Experimental results demonstrate the effectiveness of our method, compared with several state-of-the-art models including BERT-base, RoBERTa-base and T5-base.","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132589652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Integrative APP Producing an Optimal Path for the Vessel in Order to Reduce the Impacts of Cargo Ships on the Environment","authors":"Chenyu Zuo, Y. Sun","doi":"10.5121/csit.2023.130405","DOIUrl":"https://doi.org/10.5121/csit.2023.130405","url":null,"abstract":"Almost every business in the world relies in some way on the shipping industry, whether it is to ship goods or natural resources, the shipping industry is undeniably the global industry, However, these very ships that drive the economy also produce close to 1 billion metric tons of carbon dioxide per year. In this project, we explore the use of machine learning to improve the performance of cargo ships in the ocean by implementing a genetic algorithm AI and a virtual simulation environment. An app was made based on using the training developed by the AI to be able to be deployed on cargo ships as part of their navigation system. Once sufficient data regarding a vessel’s environment was collected, the algorithm could then produce an optimal path for the vessel. Experiments show that the AI system could sufficiently adjust to varying conditions and produce optimal paths for vessels.","PeriodicalId":159989,"journal":{"name":"Computer Networks & Communications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114337082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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