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43 results for “Temporal Models”
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Research ArticleOpen accessEvery word in the titleGENERALIZED LINEAR MIXED MODELS WITH SPATIAL RANDOM EFFECTS FOR SPATIO-TEMPORAL DATA: AN APPLICATION TO DENGUE FEVER MAPPINGKrisada Lekdee, Lily IngsrisawangThe Generalized Linear Mixed Models (GLMMs) with spatial random effects for spatio-temporal data are proposed. A hierarchical Bayesian method is used for parameter estimation. The random effects are assumed to be normally distributed and the spatial random effects are assumed to be proper Conditional Autoregressive (CAR) models. The proposed models are applied to Dengue fever data in Northern Thailand, including … …Journal of Mathematics and Statistics, Vol. 9, Issue 2 · 13 May 2013 · 8,065 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA Comparison of Spatio-Temporal Bayesian Models for Reconstruction of Rainfall Fields in a Cloud Seeding ExperimentSujit k. sahu, Giovanna Jona Lasinio, Arianna orasi, Kanti V. MardiaIn response to the drought experienced in Southern Italy a rain seeding project has been setup and developed during the years 1989-1994. The initiative was taken with the purpose of applying existing methods of rain enhancement technology to regions of south Italy including Puglia. The aim of this study is to provide statistical support for the evaluation of the experimental … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,810 accessesView PDFCite
Research ArticleOpen accessIn the keywordsMemory Storage Issues of Temporal Database Applications on Relational Database Management SystemsSami M. Halawani, Nashwan A. Al-RomemaProblem statement: Many existing database applications manage time-varying data. These database applications are referred to as temporal databases or time-oriented database applications that are considered as repositories of time-dependent data. Many proposals have been introduced for developing time-oriented database applications, some of which suggest building support for Temporal Database Management Systems (TDBMS) on top of existing non-temporal DBMSs, while others … …Journal of Computer Science, Vol. 6, Issue 3 · 31 Mar 2010 · 7,731 accessesView PDFCite
Research ArticleOpen accessIn the keywordsTHE TEMPORAL DYNAMICS OF REGIONAL CITY SIZE DISTRIBUTION: ANDHRA PRADESH (1951-2001)G. Kumar, A. SubbarayanSystems with measurable entities are characterized by certain properties of their size distribution. City Size Distribution (CSD) and the underlying city size dynamics have received attention in the urban economic literature in recent years. In this approach we aim at evaluating the temporal dynamics of city size distribution in Andhra Pradesh, an Indian state for the period 1951-2001. The research … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 14 Apr 2014 · 6,815 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Scalable Hybrid CNN-LSTM and SDN Based Architecture for Real-Time DDoS Defence in IoT and Cloud NetworksSujit Sutradhar, Joy Lal Sarkar, Rajesh Kumar VermaDistributed Denial-of-Service (DDoS) is one of the most significant threats to the security of IoT, cloud, and Software-Defined Network (SDN) environments because it can aggravate network unavailability and disrupt the continuity of services. This study presents a hybrid CNN-LSTM-based DDoS detection and mitigation system for real-time DDoS detection in the context of heterogeneous network environments, which is integrated with SDN … …Journal of Computer Science, Vol. 22, Issue 10 · 19 Oct 2026 · 508 accessesView PDFCite
Research ArticleOpen accessIn the abstractStudent Course Recommendation System Using Semantic Aware Temporal Gating With Time TransformerJoshi Vinay Kumar, Dahlia Sam, N. KanyaCourse recommendation system works by analyzing users’ interests and recognizes their historical behavior such as course enrollment and previously watched videos to recommend relevant courses. However, the existing course recommendation systems in Massive Open Online Courses (MOOCs) face challenges in capturing complex semantic information, which impacts student requirements with an unsatisfied recommendation. By combining historical behaviors without temporal awareness, the … …Journal of Computer Science, Vol. 22, Issue 11 · 9 Oct 2026 · 580 accessesView PDFCite
Research ArticleOpen accessIn the abstractDynamic Knowledge Driven Multi-Scale Spatiotemporal Fusion Framework for Reliable Multi-Horizon Traffic Flow ForecastingDeepika M S, P. Deepa Shenoy, Venugopal K RAccurate traffic flow forecasting remains a core challenge in intelligent transportation systems. Urban traffic patterns are characterised by complex spatiotemporal dependencies and inherent uncertainties, which complicate traffic forecasting. This study proposes a Dynamic Knowledge-Driven Spatiotemporal Fusion (DKDMSF) architecture to extract features of Multi-Scale traffic dynamics across various temporal traffic horizons. DKDMSF integrates Dynamic Spatial Attention (DSA) and Adaptive Graph Learning … …Journal of Computer Science, Vol. 22, Issue 9 · 30 Sep 2026 · 413 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 abstractA Data Mining Driven Framework for Image-Based Crop Stress Analysis Using IPCA, FP-Growth, ConvLSTM, and Sequential ModelsA. Josephine, A. SubhashiniDetecting crop stress before it becomes serious has a major impact on the food supply. It helps in avoiding the loss of crops, increases productivity and helps farmers to practice precision farming. Most technologies today are designed specifically to either detect crop stress or to predict how well the crop will grow. Many of the technologies do not combine the … …Journal of Computer Science, Vol. 22, Issue 8 · 21 Aug 2026 · 922 accessesView PDFCite
Research ArticleOpen accessIn the abstractImplementing a Hybrid VMD–BiTCN–BiGRU Architecture With Intelligent Optimization for Agricultural Commodity Price Prognostication in Gujarat MarketsJignesh Hirapara, Ratnesh Kumar Namdeo, Ankit Kumar Dubey et al.In the agricultural sector, the prices of pulses require adequate forecasting to plan farming activities, store and market, and guide policy-making. However, the pulse price series is highly volatile, seasonal, nonstationary, and nonlinear, and thus conventional linear time series models are not necessarily very effective. In the suggested framework, the raw price data series is decomposed using Variational Mode Decomposition … …Journal of Computer Science, Vol. 22, Issue 7 · 1 Aug 2026 · 1,021 accessesView PDFCite