Climate Risks Resilience Development: A Bibliometric Analysis of Climate-Related Early Warning Systems in Southern Africa

Climate, Journal Year: 2023, Volume and Issue: 12(1), P. 3 - 3

Published: Dec. 26, 2023

Early warning systems (EWS) facilitate societies’ preparedness and effective response capabilities to climate risks. Climate risks embody hazards, exposure, vulnerability associated with a particular geographical area. Building an EWS requires consideration of the factors above help people coping mechanisms. The objective this paper is propose approach that can enhance EWSs ensure risk resilience development. focuses on Southern African Development Community (SADC) region highlights issues EWS, identifying weaknesses characteristics in adaptation strategies. SADC was chosen as context because it variability change hotspot many vulnerable populations residing rural communities. Trending themes building were uncovered through scientific mapping network analysis published articles from 2008 2022. This contributes on-going research early identify hidden trends emerging technologies order operationalization design EWS. review provides insight into technological interventions for assessing build resilience. From analysis, determined there exists plethora evidence support argument involving communities co-designing would improve knowledge, anticipation, preparedness. Additionally, Fourth Industrial Revolution (4IR) provide tools address existing EWS’ weaknesses, such lack real-time data collection automation. However, 4IR technology still at nascent stage applications Africa. Furthermore, policy across societies, institutions, industries ought be coordinated integrated develop strategy toward implementing resilient-based operations disaster managers. Social, Institutional, Technology model potentially increase communities’ resilience; therefore, recommended

Language: Английский

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Bio-inspired Motion Detection Models for Improved Uav and Bird Differentiation: a Novel Deep Learning Framework

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 10, 2025

The rapid increase in Unmanned Aerial Vehicle (UAV) deployments has led to growing concerns about their detection and differentiation from birds, particularly sensitive areas like airports. Existing systems often struggle distinguish between UAVs birds due similar flight patterns, resulting high false positive rates missed detections. This research presents a bio-inspired deep learning model, the Spatiotemporal Bio-Response Neural Network (STBRNN), designed enhance real-time. model consists of three core components: Bio-Inspired Convolutional (Bio-CNN) for spatial feature extraction, Gated Recurrent Units (GRUs) capturing temporal motion dynamics, novel Layer that adjusts attention based on movement intensity, object proximity, velocity consistency. dataset used includes labeled images videos captured various environments, processed following YOLOv7 specifications. Extensive experiments were conducted comparing STBRNN with five state-of-the-art models, including YOLOv5, Faster R-CNN, SSD, RetinaNet, R-FCN. results demonstrate achieves superior performance across multiple metrics, precision 0.984, recall 0.964, F1 score 0.974, an IoU 0.96. Additionally, operates at inference time 45ms per frame, making it highly suitable real-time applications UAV bird detection.

Language: Английский

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Bio-inspired motion detection models for improved UAV and bird differentiation: a novel deep learning framework

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 3, 2025

The rapid increase in Unmanned Aerial Vehicle (UAV) deployments has led to growing concerns about their detection and differentiation from birds, particularly sensitive areas like airports. Existing systems often struggle distinguish between UAVs birds due similar flight patterns, resulting high false positive rates missed detections. This research presents a bio-inspired deep learning model, the Spatiotemporal Bio-Response Neural Network (STBRNN), designed enhance real-time. model consists of three core components: Bio-Inspired Convolutional (Bio-CNN) for spatial feature extraction, Gated Recurrent Units (GRUs) capturing temporal motion dynamics, novel Layer that adjusts attention based on movement intensity, object proximity, velocity consistency. dataset used includes labeled images videos captured various environments, processed following YOLOv7 specifications. Extensive experiments were conducted comparing STBRNN with five state-of-the-art models, including YOLOv5, Faster R-CNN, SSD, RetinaNet, R-FCN. results demonstrate achieves superior performance across multiple metrics, precision 0.984, recall 0.964, F1 score 0.974, an IoU 0.96. Additionally, operates at inference time 45ms per frame, making it highly suitable real-time applications UAV bird detection.

Language: Английский

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Towards Enhancing Computational Complexity in Explainable Decision Support System: Exploring Bio-Inspired Approaches

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 430 - 442

Published: Jan. 1, 2025

Language: Английский

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COVID-19 Pandemic Waves: 4IR Technology Utilisation in Multi-Sector Economy

Sustainability, Journal Year: 2021, Volume and Issue: 13(18), P. 10168 - 10168

Published: Sept. 10, 2021

In this paper, we reviewed the Fourth Industrial Revolution (4IR) technologies applied to waves of coronavirus disease (COVID-19). COVID-19 is an existential threat that has resulted in unprecedented loss lives, disruption flight schedules, shutdown businesses and much more. Though several researchers have highlighted enormous benefits 4IR containing pandemic, recent pandemic call for a thorough review these technological interventions. The cyber-physical space had its share effect, through review, highlight salient issues help policy formulation towards managing impact subsequent within such environments. Hence, purpose paper application during their shortcomings. Recent research articles were sourced from online repository thoroughly technology applications, innovations, shortcomings multi-sector challenges. outcome indicates second wave lower proportion patients requiring invasive mechanical ventilation rate thrombotic events. addition, it was revealed delay between ICU admissions tracheal intubation longer health care sector. Again, suggests been utilized across all sectors including education, businesses, society, manufacturing, healthcare, agriculture mining. Businesses revised service delivery models include avoid physical contacts. digital certificates, among other platforms, assist with movements persons who vaccinated. Manufacturing concerns also robots manufacturing reduce human-to-human contact. mining sector automated work processes, utilising smart boots prevent infection, bands disinfection tunnels or walkthrough sanitization gates environment. However, identified challenges implementing low-skilled workers, data privacy issues, analysis poverty, management many boom calls intense legislation on sweeping regulated tech companies. These findings hold implications tackling future outbreaks.

Language: Английский

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A Concise Review on Applications of Bioinspired Algorithms in Electrical Power System

Electric Power Components and Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: Feb. 25, 2024

The integration of bioinspired algorithms into electrical power systems has gained significant attention in recent years due to their potential address complex optimization and control problems. This paper presents a concise review on the applications various aspects systems, including generation, transmission, distribution, utilization. starts with an overview system then discusses fundamental concepts algorithms. Next, explores application generation. It examines use optimizing operation scheduling plants, maximizing renewable energy integration, improving efficiency generation processes. Moving transmission how can be applied optimize routing flows, enhance fault detection diagnosis, improve reliability security grid infrastructure. Furthermore, utilization is explored, focusing load forecasting, demand response, management, quality enhancement. Finally, concludes summary main findings future research directions. emphasizes need for further exploration development hybrid emerging technologies such as machine learning big data analytics.

Language: Английский

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Bio-Inspired Products and Technology Innovation- The Present and Future

International Journal of Innovative Research in Engineering & Management, Journal Year: 2024, Volume and Issue: 11(3), P. 25 - 33

Published: June 1, 2024

Businesses throughout the world are looking to nature for ideas when creating new goods; this is a trend that indicative of bio-innovation industry's explosive expansion. Product creation has benefited greatly from so-called "biomimicry," or mimicking ancient biological and ecological patterns principles. In too distant future, technology will not only mimic but also stand side by side. ‘Bio’ in future serve as an additional descriptor human creativity. Such creativity create more customer-friendly novel products keeping mind environmentally suitable may be made according bio-inspiration. Days far witness level beyond our wild imagination. learning about figuring out how systems operating at moment invent wildest crazy bio-inspired products. Nowadays customers mindful conscious environment, paying attention innovation sustainability. Growing green forcing companies put top priority on Bio-Technology well Green Technology signal strong customer environmentalism [1]. investing increasing amount capital research development nature-inspired solutions, product synthesizing ingenuity knowledge. The evolution exactly produce better products, fit sustainable world. Given backdrop revolution, examination required especially industry, technology, environment related one another. This paper articulates descriptive picture Bio-Inspired Innovation, businesses would connect sustainability future.

Language: Английский

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Comparison of Predictive Modeling Concrete Compressive Strength with Machine Learning Approaches

UKaRsT, Journal Year: 2024, Volume and Issue: 8(1), P. 28 - 41

Published: April 30, 2024

Accurately predicting concrete compressive strength is fundamental for optimizing mix designs, ensuring structural integrity, and advancing sustainable construction practices. Increased demands safer, more durable infrastructure necessitate effective predictive models. This research aims to compare the effectiveness of six machine learning models such as Linear Regression, Random Forest, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting, XGBoost predict strength. Used a dataset 1030 instances with varying mixture compositions, conducted extensive exploratory data analysis, applied feature engineering scaling enhance model performance. Assessments were performed 5-fold cross-validation approach R-squared (R²) metric. In addition, SHAP value used understand influence each on results. The results revealed that significantly outperformed other models, achieving an average R² 0.9178 standard deviation 0.0296. Notably, Forest Boosting also demonstrated robust capabilities. Based our experiment, these effectively predicted strengths close actual measured values, confirming their practical applicability in civil engineering. values provided insights into significant impact age cement quantity outputs. These highlight advanced ensemble methods' prediction underscore importance enhancing accuracy.

Language: Английский

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Synergizing Human and Machine

Advances in environmental engineering and green technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 249 - 282

Published: Nov. 1, 2024

Rapid technological breakthroughs in the 21st century have changed knowledge discovery and management, especially with AI. AI is great at processing massive datasets quickly accurately but lacks contextual awareness, ethical judgment, creative problem-solving. The mismatch highlights a key gap: present systems often function silos, analyzing data humans interpreting results, missing potential for deeper insights. We propose new framework combining AI's computing power human cognition. show that hybrid strategy can improve complex multidisciplinary environments using these complementary forces. According to our findings, this integration enhances efficiency generates more meaningful human-valued This research significant because it promotes dynamic iterative process, which healthcare education decision-making.

Language: Английский

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A Study of Cloud Based Solution for Data Analytics in Healthcare

2021 5th International Conference on Information Systems and Computer Networks (ISCON), Journal Year: 2023, Volume and Issue: unknown, P. 1 - 6

Published: March 3, 2023

The healthcare industry generates vast amounts of data that are crucial for improving patient outcomes and advancing medical research. However, traditional on premise solutions storage analysis can become inadequate to handle the increasing volume, variety velocity data. study aims investigate potential benefits challenges using cloud-based analytics in healthcare. This paper reports about latest development detailed role Artificial intelligence capabilities cloud Computing health care sector/industry foster innovative thinking, optimum wellbeing patient, focused medicinal support. discusses various applications, algorithms future big with a focus architecture, application applicability Hadoop Cloud such as monitoring, prediction, performance, management etc including intensive unit. many platforms, like MMAP, working this field provide fast, reliable cost effective, efficient, centric solution community issues capability forecasting impact diseases given region or nation. computing framework, along Hadoop, aids completing analytical computations identify logical, pertinent, factual trends essential strategize enhanced readiness event catastrophes by facilitating exchange among all stake holders.

Language: Английский

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