30 results for “spatial patterns”

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Research ArticleOpen accessPhrase in the titleOn Studying Spatial Patterns and Association Between these Patterns of Mortality and Prosperity in MalaysiaFaisal G. Khamis, Abdul Aziz Jemain, Kamarulzaman IbrahimIn this study, we examined the geographical distribution (the spatial structure) of mortality and prosperity in Malaysia. In addition we proposed an approach to investigate the association between clustering patterns of mortality and prosperity across different areas of the country. To characterize the geographic pattern of mortality and prosperity, three indicators (infant, neonatal, and stillbirth) were proposed for mortality and … …American Journal of Applied Sciences, Vol. 5, Issue 7 · 31 Jul 2008 · 6,935 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleDeterminants of Spatial Patterns of Sex Ratio in Haryana, IndiaSangeeta Rani, Javaid Ahmad TaliThe sex ratio is an important social indicator of gender equality and status in a society. As such, it also reveals social development of a society. In a society wherein gender discrimination is in practice and devaluation of women is prevalent, sex-selective feticide, female infanticide, death of girl child’s due to neglect and discrimination in food as well as in … …Journal of Social Sciences, Vol. 13, Issue 1 · 31 Jan 2017 · 8,180 accessesView PDF Cite

Research ArticleOpen accessEvery word in the titleSummarizing Spatial Distribution Density, Movement Patterns and Food Resources to Study the Impacts of Logging and Forest Conversion on Orang-utan PopulationRaymond Alfred, Koh Pei Hue, Lee Shan Khee, Rayner AlfredProblem statement: Orang-utan is classified as a totally protected species and is listed as an endangered species in Borneo. The survival of this species is highly dependent on the existence and quality of the lowland forest of Sabah. However, most of the pristine habitats in the lowland area have been converted into other land use activities such as a large … …OnLine Journal of Biological Sciences, Vol. 10, Issue 2 · 31 Oct 2010 · 8,184 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsAn Efficient Algorithm for Mining Spatially Co-located Moving ObjectsG. Manikandan, S. SrinivasanMining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took much computational cost. In … …American Journal of Applied Sciences, Vol. 10, Issue 3 · 2 Mar 2013 · 6,649 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsDistribution and Determining of Tourist Attractions and Modeling of Tourist Cities for the City of Isfahan-IranSeifolddini-Faranak, M. Shabani Fard, Hosseini AliProblem statement: Cities have different attractions and usually have tourists. But Management and development sustainable tourist activities needs planning. Approach: This research used survey method and taxonomy analysis to study the spatial pattern of tourist attractions in Isfahan city. Isfahan city is chosen because of numerous tourist attractions and its importance at national and regional level and also its multi … …American Journal of Economics and Business Administration, Vol. 1, Issue 2 · 30 Jun 2009 · 7,673 accessesView PDF Cite

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 PDF Cite

Research ArticleOpen accessIn the abstractTAGN-IDC: Implicit Discrete Constraints for Tsunami Aware Graph Neural Networks in Spatial Evacuation RoutingSularno, Wendi Boy, Putri Anggraini et al.Tsunami evacuation route requires rapid identification of safe evacuation paths under dynamically changing hazard conditions. A major challenge is incorporating hard binary road-blockage constraints caused by tsunami inundation while maintaining end-to-end differentiability for deep learning models. Existing approaches commonly rely on continuous relaxations or stochastic sampling techniques, which either compromise physical feasibility or increase computational complexity. This study proposes the … …Journal of Computer Science, Vol. 22, Issue 9 · 24 Sep 2026 · 704 accessesView PDF Cite

Research ArticleOpen accessIn the abstractAnchoring Public Health Forecasts: High-Precision Obesity Prevalence Estimation Via Hybrid Geodemographic Ensemble RegressionTrenton Ward, Isaac OsunmakindeObesity remains a major driver of chronic disease and rising healthcare costs in the United States. Yet, most existing predictive models rely on single-model classifiers and rarely incorporate spatial structure or ensemble-based regression, thereby limiting their ability to capture geodemographic variation. This study introduces a hybrid unsupervised–supervised ensemble framework that integrates K-Means spatial profiling with a Stacking Regressor to address … …Journal of Computer Science, Vol. 22, Issue 7 · 31 Jul 2026 · 632 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDeep Learning Synergy: CNNs and Transformers for Epidemic Outbreak ForecastingAlexander R., Shakthi Priya V., Sumathi G. et al.An Accurate prediction of outbreaks is extremely crucial for taking proactive public health interventions, distributing limited resources, and controlling a disease. This paper assesses and also compares the performances of the available deep learning models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) networks, and Bidirectional Long Short-Term Memory (BiLSTM) networks, in predicting. CNNs are best for feature extraction … …Journal of Computer Science, Vol. 21, Issue 12 · 23 Jan 2026 · 2,113 accessesView PDF Cite

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 PDF Cite