Journal of Graphic Era University最新文献

A Fast and robustColor Image Encryption Scheme by Huffman Compression, 5D Chaotic Map and DNA Encoding 利用哈夫曼压缩、5D 混沌图谱和 DNA 编码的快速稳健彩色图像加密方案

Journal of Graphic Era University Pub Date : 2024-07-15 DOI: 10.13052/jgeu0975-1416.1221

S. Navaneethan, G. Kalamathipriya, M. S. M. Keyasudeen, K. Parthiban

引用次数: 0

Fractals as Julia and Mandelbrot Sets via S-iteration 通过 S-iteration 将分形视为朱莉娅集和曼德尔布罗特集

Journal of Graphic Era University Pub Date : 2024-07-15 DOI: 10.13052/jgeu0975-1416.1222

Sanjay Kumar Padaliya, Saurabh Sharma, Jasvinder Pal Singh

引用次数: 0

Some Studies on Cutting Force Generation in Machining of Mg-Ca1.0 Alloy 关于加工 Mg-Ca1.0 合金时产生的切削力的一些研究

Journal of Graphic Era University Pub Date : 2024-04-16 DOI: 10.13052/jgeu0975-1416.1215

K. Jagtap, Suresh Sanap

引用次数: 0

Shortest Path of a Random Graph and its Application 随机图的最短路径及其应用

Journal of Graphic Era University Pub Date : 2024-04-16 DOI: 10.13052/jgeu0975-1416.1214

Laxminarayan Sahoo, Rakhi Das

{"title":"Shortest Path of a Random Graph and its Application","authors":"Laxminarayan Sahoo, Rakhi Das","doi":"10.13052/jgeu0975-1416.1214","DOIUrl":"https://doi.org/10.13052/jgeu0975-1416.1214","url":null,"abstract":"The goal of this work is to provide an effective method for determining the shortest path in random graphs, which are complicated networks with random connectivity patterns. We have developed an algorithm that can identify the shortest path for both weighted and unweighted random graphs to accomplish our objective. As connectivity in these types of structures is changing, the algorithm adjusts to different edge weights and node configurations to provide fast and precise shortest path searching. The study shows that the suggested method performs more successfully in finding the shortest path throughout random graphs using comprehensive computations. Many networks, including social networks, granular networks, road traffic networks, etc., include nodes that can connect to one another and create random graphs in the present-day computational era. The outcomes demonstrate how flexible it is, which makes it a useful tool for practical uses in domains where random graph structures are common, like transportation networks, communication systems, and social networks. For illustration, we have taken into consideration an actual case study of communication road networks here. We have determined the shortest path of the road networks using our proposed algorithm, and the results have been presented. Better decision-making across a range of areas is made possible by this study, which advances effective algorithms designed for complicated and unpredictable network environments.","PeriodicalId":142472,"journal":{"name":"Journal of Graphic Era University","volume":"3 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697846","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}

引用次数: 0

Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model 使用混合机器学习模型改进零售店需求预测

Journal of Graphic Era University Pub Date : 2023-11-20 DOI: 10.13052/jgeu0975-1416.1212

Vinit Taparia, Piyush Mishra, Nitik Gupta, Devesh Kumar

{"title":"Improved Demand Forecasting of a Retail Store Using a Hybrid Machine Learning Model","authors":"Vinit Taparia, Piyush Mishra, Nitik Gupta, Devesh Kumar","doi":"10.13052/jgeu0975-1416.1212","DOIUrl":"https://doi.org/10.13052/jgeu0975-1416.1212","url":null,"abstract":"Accurate demand forecasting is a competitive advantage for all supply chain components, including retailers. Approaches like naïve, moving average, weighted average, and exponential smoothing are commonly used to forecast demand. However, these simple approaches may lead to higher inventory and lost sales costs when the trend in demand is non-linear. Additionally, price strongly influences demand, and we can’t neglect the impact of price on demand. Similarly, the demand for a stock keeping unit (SKU) depends on the price of the competitor for the same SKU and the price of the competitive SKU. We thus propose a demand prediction model that considers historical demand data and the SKU price to forecast the demand. Our approach uses different machine-learning regressor algorithms and identifies the best machine-learning algorithm for the SKU with the lowest forecasting error. We further extend the forecasting model by training a hybrid model from the best two regression algorithms individually for each SKU. Forecasting error minimisation is the driving criterion for our literature. We evaluated the approach on 1000 SKUs, and the result showed that the Random Forest is the best-performing regressor algorithm with the lowest mean absolute percentage error (MAPE) of 8%. Furthermore, the hybrid model resulted in a lower inventory and lost sales cost with a MAPE of 7.74%. Overall, our proposed hybrid demand forecasting model can help retailers make informed decisions about inventory management, leading to improved operational efficiency and profitability.","PeriodicalId":142472,"journal":{"name":"Journal of Graphic Era University","volume":"62 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254664","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}

引用次数: 0

Data-Driven Retail Excellence: Machine Learning for Demand Forecasting and Price Optimization 数据驱动的卓越零售：针对需求预测和价格优化的机器学习

Journal of Graphic Era University Pub Date : 2023-11-20 DOI: 10.13052/jgeu0975-1416.1213

Vinit Taparia, Piyush Mishra, Nitik Gupta, Hitesh Chandiramani

{"title":"Data-Driven Retail Excellence: Machine Learning for Demand Forecasting and Price Optimization","authors":"Vinit Taparia, Piyush Mishra, Nitik Gupta, Hitesh Chandiramani","doi":"10.13052/jgeu0975-1416.1213","DOIUrl":"https://doi.org/10.13052/jgeu0975-1416.1213","url":null,"abstract":"Demand forecasting and price optimization are critical aspects of profitability for retailers in a supply chain. Retailers need to adopt innovative strategies to optimize pricing and increase profitability. This research paper proposes a price optimization approach for retailers using machine learning. The approach involves using linear regression to forecast demand incorporating price as an input, followed by price optimization taking into account inventory and perishability costs. The feasibility of using linear regression for price optimization for Stock Keeping Units (SKUs) is assessed using a feasibility index. The linear regression can predict the demand more accurately (23% Mean Absoulute Percentage Error (MAPE)) compared to exponential smoothing with optimised smoothing constant (47.09% MAPE) for 1000 SKUs. Also, the feasibility index can segregate the SKUs with an accuracy of 99%. The machine learning-based demand forecasting can assist retailers in accurately predicting customer demand and improving pricing decisions, while the feasibility index enables retailers to identify SKUs that require alternative pricing strategies.","PeriodicalId":142472,"journal":{"name":"Journal of Graphic Era University","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255842","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}

引用次数: 0

Blockchain Technology Enabled Traceability Framework: Food Supply Chain Perspective 区块链技术驱动的可追溯性框架：食品供应链视角

Journal of Graphic Era University Pub Date : 2023-11-20 DOI: 10.13052/jgeu0975-1416.1211

D. Ghode, A. A. Wagire, Pravin A. Dwaramwar, Rajesh H. Khobragade

引用次数: 0

Silver, Bi2Te3 and Antimonene Based Surface Plasmon Resonance Sensor for Enhancement of Sensitivity 基于银、Bi2Te3和锑烯的表面等离子体共振传感器的灵敏度增强

Journal of Graphic Era University Pub Date : 2023-08-03 DOI: 10.13052/jgeu0975-1416.1124

Arun Uniyal, Brajlata Chauhan, A. Pal, Sanjiv Tomar, Priyanka Dobhal Uniyal

引用次数: 2

Modelling of Artificial Neural Network to Validate the Experimental Data of a Laboratory Scale ROT 人工神经网络建模以验证实验室规模ROT的实验数据

Journal of Graphic Era University Pub Date : 2023-08-03 DOI: 10.13052/jgeu0975-1416.1126

P. Biswas, K. Chakraborty, Pratik Kumar Raha, P. Mandal

引用次数: 0

Propagation of Love Waves Through an Irregular Surface of a Layered Poroelastic Rock Structure Love波通过层状孔隙弹性岩石结构不规则表面的传播

Journal of Graphic Era University Pub Date : 2023-08-03 DOI: 10.13052/jgeu0975-1416.1123

A. Negi, S. Kapoor

引用次数: 0