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Research ArticleOpen accessIn the abstractSales Forecasting Models: Comparison between ARIMA, LSTM and ProphetDmitry BrykinSales forecasting is crucial for business planning and resource allocation. Data-driven approaches have become popular in this field. This study compares the performance of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Prophet within the context of specific sales categories derived from acquiring data provided by a bank. This study uses a time series dataset … …Journal of Computer Science, Vol. 20, Issue 10 · 1 Aug 2024 · 7,981 accessesView PDFCite
Research ArticleOpen accessIn the abstractNattokinase Activity Guided Natto Fermentation Process OptimizationXiaojie Ren, Chao Huang, Xin Shan et al.The natto kinase in natto has obvious thrombolytic effect, has the advantages of low cost, small side effects, long half-life and can be taken orally, so it has attracted much attention. At present, the research on natto kinase in our country mainly focuses on the selection of natto fermentation strains, the optimization of gene expression conditions and the exploration of … …American Journal of Biochemistry and Biotechnology, Vol. 17, Issue 4 · 20 Dec 2021 · 7,151 accessesView PDFCite
Research ArticleOpen accessIn the abstractStatistical Tool for Testing Agreement Level on Continuous DatasetsBasavarajaiah Mariyappa Doddagangavadi, B. Narasimha Murthy, Netra RajpurohitVarious analytical studies explored the new innovation for testing agreement level in medical and life sciences which can be simulated by Cohen ‘κ’ based on the practical applications. From the past medical literature, many authors suggested that, there is some disproportion research gaps that exists in the statistical methods for measuring the agreements between two or more observers from ‘Cohen … …Current Research in Biostatistics, Vol. 11, Issue 1 · 19 May 2021 · 11,780 accessesView PDFCite
Research ArticleOpen accessIn the abstractContext Tamil Llama-QA: A Question and Answering Framework for Semantic Information Retrieval Using Tamil Large Language ModelJeevit Davidson S., M. MuraliThe Question Answering System (QA) is essential for effectively retrieving and providing relevant information, and its performance is heavily reliant on the efficiency of information retrieval mechanisms. As the volume of information within the corpus continues to grow, the complexity of generating accurate and pertinent answers also increases substantially. This challenge is particularly significant when designing QA systems for regional … …Journal of Computer Science, Vol. 22, Issue 10 · 7 Oct 2026 · 442 accessesView PDFCite
Research ArticleOpen accessIn the abstractIntegration of Local Large Language Models, Retrieval-Augmented Generation, and Adaptive LearningAnass Belcaid, Kamal ReklaouiIn recent years, many schools and teachers have started using closed Large Language Models (LLMs) to help with learning. These tools can be very helpful for tutoring and personal learning, but they also bring serious problems. One big issue is that they use cloud systems, which means student data is sent to outside servers. This can put privacy at risk … …Journal of Computer Science, Vol. 22, Issue 4 · 26 Mar 2026 · 3,923 accessesView PDFCite
Research ArticleOpen accessIn the abstractIoT-Based Smart Hydroponic System for Nutrient Management Using Predictive Machine Learning AlgorithmsPalash Gourshettiwar, K. T. V. ReddyHydroponics, an efficient cultivation method, benefits significantly from the precision and adaptability that Machine Learning (ML) algorithms can offer, along with the real-time monitoring facilitated by the devices using the Internet of Things (IoT). This study summarises the latest research and discusses how Machine Learning and the IoT work together, focusing on nutrient optimization, plant development, and resource efficiency. The … …Journal of Computer Science, Vol. 22, Issue 3 · 11 Mar 2026 · 3,046 accessesView PDFCite
Research ArticleOpen accessIn the abstractTime Series Analysis for Predicting Tea Harvest Yield: A SARIMAX-Based ApproachPallavi Nagpal, Deepika Chaudhary, Jaiteg SinghPrecise prediction of tea yield is crucial for both agricultural planning and economic forecasting. Projection of the future trends, which rely on present and historical data, is the process termed 'forecasting'. Yield prediction is fundamental for research and development. By gathering the yield data over historic times, the researchers can make their work more valuable by predicting the yield patterns. … …Journal of Computer Science, Vol. 22, Issue 2 · 19 Feb 2026 · 1,891 accessesView PDFCite
Research ArticleOpen accessIn the abstractDetermining the Time Required to Detect Induced Sow Lameness Using an Embedded Microcomputer-Based Force Plate SystemBrady McNeil, Julia Calderón Díaz, Caitlyn Bruns et al.Lameness in swine breeding herds is a common cause of compromised animal well-being and economic loss to pig producers. Current lameness assessment methods are subjective and require intensive training. It has been shown that embedded microcomputer-based force plate systems can detect lameness by measuring weight distributions in livestock. The objective of this study was to determine the minimum time required … …American Journal of Animal and Veterinary Sciences, Vol. 13, Issue 2 · 25 May 2018 · 7,612 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Time Series Modeling to Study River Water QualityMaryam Ghashghaie, Kaveh Ostad-Ali-Askari, Saeid Eslamian, Vijay P. SinghWater deficit problem originates from two factors: population increase and water pollution. However, studying and forecasting the quality of water are necessary to avoid serious problems in future through managerial works. In present study, using time series modeling, the quality of Madian Rood River is studied at Baraftab station using time series analysis. Nine parameters of water quality are studied … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 2 · 25 Apr 2018 · 8,933 accessesView PDFCite
Research ArticleOpen accessIn the abstractConvolution Neural Networks of Dynamically Sized Filters with Modified Stochastic Gradient Descent Optimizer for Sound ClassificationManu Pratap Singh, Pratibha RashmiDeep Neural Networks (DNNs), specifically Convolution Neural Networks (CNNs) are found well suited to address the problem of sound classification due to their ability to capture the pattern of time and frequency domain. Mostly the convolutional neural networks are trained and tested with time-frequency patches of sound samples in the form of 2D pattern vectors. Generally, existing pre-trained convolutional neural … …Journal of Computer Science, Vol. 20, Issue 1 · 13 Dec 2023 · 5,781 accessesView PDFCite