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82 results for “Learning Parameters”
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Research ArticleOpen accessIn the abstractDecentralized Lean Business Model Canvas for Blockchain-Based EnterprisesEashwar Sivakumar, Paras ChawlaAgeneric decentralized lean business model canvas was developed using theprinciples of business process management and lean six sigma forblockchain-based systems presented in this study. Based on the literaturereview, it was found that business model canvas and lean canvas are some of themost widely used management tools. Business Model Canvas can be used forselling a specific product and lean canvas can … …Journal of Computer Science, Vol. 18, Issue 5 · 5 Jun 2022 · 7,448 accessesView PDFCite
Research ArticleOpen accessIn the abstractHMA-RAG: A Hierarchical Memory Augmented Retrieval Augmented Generation Framework for Fake News DetectionAfnan Altarjami, Sawsan AlshattnawiExisting methods for detecting fake news, such as traditional machine learning and transformer-based models, often work as separate classifiers that do not use outside evidence, historical knowledge, or ways to make the results easier to understand. This paper presents a framework that combines retrieval augmentation and hierarchical memory to overcome three primary constraints: Insufficient external evidence, the absence of memory, … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 735 accessesView PDFCite
Research ArticleOpen accessIn the abstractEmploying Hybrid Serial Cascaded Adaptive Network for Anomaly Detection and Prevention in IoT Time Series Data With Optimal Interdomain RoutingKante Satyanarayana, K. VenkateshNowadays, emerging Internet of Things (IoT) in data processing becomes interested research topic and is used for various applications. This varies from big data processing and analytics for accumulating entire sensor data to detect and generate long-term trends. Nevertheless, this results in the requirement for resources like the execution of power, memory, and bandwidth, in which resource-constrained IoT tools suffers … …Journal of Computer Science, Vol. 22, Issue 1 · 21 Feb 2026 · 1,466 accessesView PDFCite
Research ArticleOpen accessIn the abstractBangla↔English Machine Translation Using Attention-based Multi-Headed Transformer ModelArgha Chandra Dhar, Arna Roy, M. A. H. Akhand et al.Machine Translation (MT) refers to translate texts or documents from the source language into the target language without human intervention. Any MT model is language-dependent and its development requires grammar, phrase rules, vocabulary, or relevant data for the particular language pair. Hitherto, little research on MT for Bangla-English is reported in the literature, although Bangla is a major language. This … …Journal of Computer Science, Vol. 17, Issue 10 · 10 Nov 2021 · 8,469 accessesView PDFCite
Research ArticleOpen accessIn the abstractExploring Learning Capability of an Agent in SOAR: Using 8-Queens ProblemNeha Rajan, Sunderrajan SrinivasanCognitive architecture deals with describing the intelligent behavior of an agent. The description of intelligent behavior states how well an agent can solve and represent variety of problems of the domain-independent task. The intelligence of an agent is considered in terms of its learning capabilities. In this study, we are exploring solving 8-Queens combinatorial problem using SOAR symbolic cognitive architecture. … …Journal of Computer Science, Vol. 16, Issue 5 · 25 May 2020 · 7,067 accessesView PDFCite
Research ArticleOpen accessIn the abstractIoT Framework for Smart Irrigation using Machine Learning TechniqueS. Ramya, A.M. Swetha, Manivannan DoraipandianThe scarcity of freshwater around the world is becoming a serious issue, so there is a dire need to come up with a solution for effective utilization of water resources. Basically, agriculture depends on monsoon which is not a sufficient water source. The Internet of Things (IoT) and Machine Learning (ML) based smart irrigation system is employed in the field … …Journal of Computer Science, Vol. 16, Issue 3 · 26 Mar 2020 · 9,654 accessesView PDFCite
Research ArticleOpen accessIn the abstractNon-Hodgkin Lymphoma Risk Grading Through the Pathological Data by Using the Optimized Convolutional Lymphnet ModelSivaranjini Nagarajan, Gomathi MuthuswamyDiagnosing Non-Hodgkin Lymphoma (NHL) is difficult and often requires specialised training and expertise as well as extensive morphological investigation and, in certain cases, costly immunohistological and genetic techniques. Computational approaches enabling morphological-based decision making are necessary for bridging the existing gaps. Histopathological images can be accurately classified using deep learning approaches, however data on NHL subtyping is limited. However, there … …Journal of Computer Science, Vol. 20, Issue 5 · 28 Feb 2024 · 4,545 accessesView PDFCite
EditorialOpen accessIn the abstractImpact of Quantitative Pharmacology on Drug DevelopmentAna Ruiz-Garcia, Kourosh ParivarThesuccess of drug development rests over two well defined pillars, a goodunderstanding of the drug’s clinical pharmacology and the appropriate targetpopulation for whom the clinical use is intended. The label for human prescription drugsrequires a good understanding of the pharmacological effects (pharmacodynamicsor PD) and the mechanism of action of the drug as well as detailed informationof the drug’s Pharmacokinetics (PK): … …American Journal of Pharmacology and Toxicology, Vol. 9, Issue 4 · 24 Apr 2015 · 7,158 accessesView PDFCite
Research ArticleOpen accessIn the abstractNeural Network in Modeling Malaysian Oil Palm YieldZuhaimy Ismail, Azme KhamisProblem statement: Forecasting of palm oil yield has become an important element in the management of oil palm industry for proper planning and decision making. The importance of yield forecasting has led us to explore modeling of palm oil yield for Malaysia using the most recent development of Artificial Neural Network (ANN). The main issue in yield forecasting is to … …American Journal of Applied Sciences, Vol. 8, Issue 8 · 19 Jul 2011 · 8,576 accessesView PDFCite
Research ArticleOpen accessIn the abstractNeural Network Change Detection Model for Satellite Images Using Textural and Spectral CharacteristicsA. K. Helmy, Gh. S. El-TaweelProblem statement: Change detection is the process of identifying difference of the state of an object or phenomena by observing it at different time. Essentially, it involves the ability to quantify temporal effects using multi-temporal data sets. Information about change is necessary for evaluating land cover and the management of natural resources. Approach: A neural network model based on both … …American Journal of Engineering and Applied Sciences, Vol. 3, Issue 4 · 4 Nov 2010 · 8,156 accessesView PDFCite