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57 results for “Anomalies”
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Research ArticleOpen accessIn the keywordsChemical and Statistical Analyses of Elements in River Water of Morava e BincesFatbardh Gashi, Naser Troni, Fatmir Faiku et al.The objective of this research was assessment of environmental toxic elements downstream the river of Morava e Binces (Kosovo) and this study is a continuation of earlier studies of surface waters in our country. The sampling and analysis are conducted in accordance with EU and WHO standards. This study uses both quantitative and qualitative analysis to assess the water quality … …American Journal of Environmental Sciences, Vol. 9, Issue 2 · 8 Apr 2013 · 8,672 accessesView PDFCite
Research ArticleOpen accessIn the abstractAnalysis of the 16-20 February 2025 Damaging Heavy Rainfall in BotswanaOliver MosesHeavy rainfall that occurred from 16-20 February 2025 over most of Botswana caused, for example, flash flooding, fatalities, and widespread displacement, with the most severe impacts in the Gaborone-Mochudi region in the southeastern parts of the country. The drivers of this event are poorly understood; hence, this study analyses the rainfall characteristics using CHIRPS and station data. It also analyses … …American Journal of Applied Sciences, Vol. 23, Issue 1 · 24 Aug 2026 · 1,111 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 abstractSpatio-Temporal Anomaly Detection in Groundwater Electrical Conductivity Using a Hybrid Framework of Isolation Forest and AutoencoderEunji Lee, Seunghyun Lim, Seojun Lee, Abhijit DebnathMonitoring groundwater quality is vital for environmental safety and resource sustainability. This study combines Isolation Forest and Autoencoder models to detect anomalies in Electrical Conductivity (EC) and temperature, using monthly data collected in South Korea between 2006 and 2023. Linear regression and the Mann-Kendall test reveal a weak, episodic downward EC trend. Seasonal decomposition indicates annual cyclicality, while residual analysis … …Journal of Computer Science, Vol. 22, Issue 1 · 14 Feb 2026 · 1,918 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Dynamically Zero Trust and Risk Aware Cryptographic Orchestration Framework of Secure Multi Cloud Electronic Health RecordsD. Veerabhadra Rao, G. Appa Rao, S. AnuradhaThe adoption of multi-cloud infrastructures for Electronic Health Records (EHRs) improves scalability and accessibility but introduces security challenges such as fragmented trust management, high cryptographic overhead, and inefficient integrity verification. Existing frameworks rely on static authorization and uniform encryption mechanisms, which fail to dynamically evaluate behavioural risks associated with user access requests. Consequently, Distributed Trust Inconsistency, Cryptographic Scalability Saturation, and … …Journal of Computer Science, Vol. 22, Issue 10 · 7 Oct 2026 · 453 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 abstractClimate-Driven Productivity Responses and Implications for Genetic Adaptation and Breeding of Boreal Trees in KazakhstanZhadyra Baigazakova, Kuralay Mazarzhanova, Asset Satayev et al.Kazakhstan’s boreal and forest-steppe forests form the southern ecotonal edge of the West Siberian taiga, where relatively small changes in heat and moisture can trigger disproportionate impacts on tree growth, mortality, and regeneration. Although forests cover only ~4% of Kazakhstan, they provide critical ecosystem services: stabilizing hydrological regimes, buffering microclimates, protecting agricultural lands from wind and water erosion, and supporting … …OnLine Journal of Biological Sciences, Vol. 26, Issue 3 · 10 Aug 2026 · 709 accessesView PDFCite
Research ArticleOpen accessIn the abstractOptimization of Non-Technical Electricity Loss Detection Using Artificial Neural Networks: Case of Cameroon Distribution NetworkLekini Nkodo Claude Bernard, Bell Serge Samuel, Nyemb Nsoga Valjacques et al.Non-Technical Losses (NTL) represent a critical challenge for power utilities, particularly in developing countries. This paper proposes an unsupervised deep learning approach based on an autoencoder to detect abnormal electricity consumption patterns in the Cameroonian distribution network. The model learns normal customer behavior using historical consumption data and identifies anomalies through reconstruction error analysis. Statistical thresholds are applied to classify … …American Journal of Engineering and Applied Sciences, Vol. 19, Issue 1 · 23 Jul 2026 · 1,225 accessesView PDFCite
Research ArticleOpen accessIn the abstractSmart and Secure Anomaly Detection in IoT Using Quantum Inspired Kernel Ensemble LearningAbrar M. AlajlanThe exponential growth of the Internet of Things networks has greatly increased the attack surface for cyber threats, necessitating the use of strong and perceptive anomaly detection systems. Traditional intrusion detection systems often struggle with high false alarm rates, limited generalization, and inefficiency in processing high-dimensional heterogeneous IoT data. To address this issue, a novel Hybrid Quantum-Enhanced Kernel Ensemble Learning … …Journal of Computer Science, Vol. 22, Issue 3 · 12 Mar 2026 · 1,762 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Distillation GAN Model for Network Intrusion DetectionChandrakala C B, Sai Sreevall, Raghudathesh G PThis study presents a distillation-based Generative Adversarial Network (GAN) model designed for intrusion detection in network systems. A Teacher-Student architecture is proposed, in which a single teacher generator is utilized to train both the teacher discriminator and the student discriminator. The model was trained to identify anomalies present in network traffic intrusions by tuning the min-max loss function in GANs. … …Journal of Computer Science, Vol. 22, Issue 2 · 5 Mar 2026 · 1,549 accessesView PDFCite