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Journal articles on the topic 'Threat Forecasting'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Krutika Dwarka Naidu, Dr. Syed Irfan Ali, Sujal Shyam Hasoriya, and Sujal Ganvir. "Threat Foresight: Web Threat Detection and Forecasting Trends and Insights." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 129–33. https://doi.org/10.32628/ijsrst25122209.

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The increasing sophistication and frequency of web threats necessitate advanced analytics and forecasting techniques to mitigate potential cyber risks. Traditional security measures, while effective to some extent, often struggle to adapt to evolving cyber threats. The advent of Artificial Intelligence (AI) and Generative AI (GenAI) has introduced novel methodologies for detecting, analyzing, and predicting web-based threats. This review paper explores the landscape of web threat analytics, evaluates traditional and modern forecasting techniques, and examines the role of AI and GenAI in enhanc

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Aleksandrowicz, Tomasz. "Forecasting terrorist threats as an element of the anti-terrorist system." Terroryzm, no. 6 (6) (October 9, 2024): 405–27. http://dx.doi.org/10.4467/27204383ter.24.028.20248.

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The article is devoted to the problem of forecasting terrorist threats and its role in the state’s anti-terrorist system. The aim of the article is to present the methodology of forecasting terrorist threats at three levels: strategic, operational and tactical. The text presents basic methods, techniques and tools used in threats forecasting. The main thesis of the article is the statement that a properly prepared forecast allows to determine with a high degree of probability the possibility of a terrorist threat, however, in such a case we are always dealing with the assessment of such a poss

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Aleksandrowicz, Tomasz. "Prognozowanie zagrożeń terrorystycznych jako element systemu antyterrorystycznego." Terroryzm, no. 6 (6) (October 9, 2024): 127–50. http://dx.doi.org/10.4467/27204383ter.24.018.19406.

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The article is devoted to the problem of forecasting terrorist threats and its role in the state’s anti-terrorist system. The aim of the article is to present the methodology of forecasting terrorist threats at three levels: strategic, operational and tactical. The text presents basic methods, techniques and tools used in threats forecasting. The main thesis of the article is the statement that a properly prepared forecast allows to determine with a high degree of probability the possibility of a terrorist threat, however, in such a case we are always dealing with the assessment of such a poss

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Kervyn, Nicolas, Susan Fiske, and Vincent Yzerbyt. "Forecasting the Primary Dimension of Social Perception." Social Psychology 46, no. 1 (2015): 36–45. http://dx.doi.org/10.1027/1864-9335/a000219.

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The Stereotype Content Model (SCM) posits two fundamental dimensions of intergroup perception, warmth and competence, predicted by socio-structural dimensions of competition and status, respectively. However, the SCM has been challenged on claiming perceived competition as the socio-structural dimension that predicts perceived warmth. The current research improves by broadening warmth’s predictor (competition) to include both realistic and symbolic threat from Integrated Threat Theory (Study 1). We also measure two components of the warmth dimension: sociability and morality. Study 2 tests new

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Egho-Promise, Ehigiator, George Asante, Hewa Balisane, et al. "Leveraging Artificial Intelligence for Predictive CyberSecurity: Enhancing Threat Forecasting and Vulnerability Management." International Journal of Innovative Research in Advanced Engineering 12, no. 02 (2024): 68–79. https://doi.org/10.26562/ijirae.2025.v1202.01.

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The rise in sophisticated cyber threats demands advanced cybersecurity methods that surpass traditional rule-based approaches. This study explores the application of Artificial Intelligence (AI) to enhance predictive cybersecurity, enabling more accurate threat forecasting and effective vulnerability management. The research assesses various AI modelssuch as neural networks, decision trees, and Support Vector Machines (SVMs) in their ability to predict cyber threats. Employing a quantitative methodology, the study utilizes historical data from cybersecurity sources, threat intelligence feeds,

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McCaul, Eugene W., Steven J. Goodman, Katherine M. LaCasse, and Daniel J. Cecil. "Forecasting Lightning Threat Using Cloud-Resolving Model Simulations." Weather and Forecasting 24, no. 3 (2009): 709–29. http://dx.doi.org/10.1175/2008waf2222152.1.

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Abstract Two new approaches are proposed and developed for making time- and space-dependent, quantitative short-term forecasts of lightning threats, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the Weather Research and Forecasting (WRF) model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to

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Korystin, Oleksandr, Igor Tsiupryk, and Oleksandr Nikolaiev. "FORECASTING THE RISKS OF UNCONTROLLED DEFORESTATION IN UKRAINE." Baltic Journal of Economic Studies 10, no. 4 (2024): 261–70. https://doi.org/10.30525/2256-0742/2024-10-4-261-270.

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The forestry sector in Ukraine is currently confronted with a multitude of challenges, including the repercussions of climate change, ecological issues, economic challenges, and the consequences of military actions initiated by the Russian Federation, which have caused extensive damage to forests across the country. This highlights the necessity for research aimed at assessing threats and risks in the forestry sector, as well as evaluating the institutional capacity to ensure the sustainable development of the industry. The research was conducted in accordance with the mandate of the Temporary

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Regens, James L., Nicholas A. Mould, Carl J. Jensen, and David N. Edger. "Terrorism-Centric Behavior Recognition and Adversarial Threat Forecasting." International Journal of Intelligence and CounterIntelligence 29, no. 2 (2016): 328–40. http://dx.doi.org/10.1080/08850607.2015.1083317.

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Boucher, Kathryn L., Robert J. Rydell, and Mary C. Murphy. "Forecasting the experience of stereotype threat for others." Journal of Experimental Social Psychology 58 (May 2015): 56–62. http://dx.doi.org/10.1016/j.jesp.2015.01.002.

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Tkachuk, Natalia. "Analytical Component in Institutional Providing of Ukraine National Security Risks and Threats Assessment." Information Security of the Person, Society and State, no. 28-30 (December 15, 2020): 38–44. http://dx.doi.org/10.51369/2707-7276-2020-(1-3)-5.

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Today, in many developed countries, the analytical component in the institutional support of risk assessment and threats to national security is implemented by situational centers (SCs) at the strategic level through scientific-analytical and predictive support of the national security threat planning process. On the other hand, the common feature of such domestic situational centers is that they were created and continue to function mainly for operational management in the «manual» mode. Whereas the solution of the tasks of strategic analysis and forecasting in the field of national security

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LYSOV, B. S., V. H. HUSKOVA, and A. A. KHALYGOV. ""APPLICATION OF INTELLIGENT DATA ANALYSIS APPROACHES FOR PRELIMINARY CYBER‑THREAT ASSESSMENT OF CRITICAL INFRASTRUCTURE FACILITIES"." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 1, no. 40 (2025): 28–34. https://doi.org/10.31474/1996-1588-2025-1-40-28-34.

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"Based on current requirements for time‑series analysis in cyber‑threat detection, this paper examines forecasting methods for identifying anomalous requests to critical‑infrastructure assets. Special attention is paid to building models that account for both the current traffic dynamics and its rate of change. The approaches analyzed include AutoRegressive (AR), AutoRegressive Moving Average (ARMA), AutoRegressive Integrated Moving Average (ARIMA), and a sequential model with linear layer stacking. To ensure forecasting accuracy, we propose testing data stationarity and applying decomposition

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12

Fkih, Fethi, and Ghadeer Al-Turaif. "Threat Modelling and Detection Using Semantic Network for Improving Social Media Safety." International Journal of Computer Network and Information Security 15, no. 1 (2023): 39–53. http://dx.doi.org/10.5815/ijcnis.2023.01.04.

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Social media provides a free space to users to post their information, opinions, feelings, etc. Also, it allows users to easily and simultaneously communicate with each other. As a result, threat detection in social media is critical for ensuring the user’s safety and preventing suspicious activities such as criminal behavior, hate speech, ethnic conflicts and terrorist plots. These suspicious activities have a negative impact on the community’s life and cause tension and social unrest among individuals in both inside and outside of cyberspace. Furthermore, with the recent popularity of social

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Pasupuleti, Murali Krishna. "Smart Load Balancing and Real-Time Threat Prediction for National Energy Infrastructure." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 243–50. https://doi.org/10.62311/nesx/rp1925.

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Abstract: The increasing complexity and demand on national energy infrastructures necessitate advanced solutions for efficient load management and threat mitigation. This paper explores the integration of Artificial Intelligence (AI) in smart load balancing and real-time threat prediction within national energy grids. By leveraging AI algorithms, energy systems can dynamically forecast demand, optimize energy distribution, and preemptively identify potential threats, ensuring stability and resilience. The study delves into current methodologies, challenges, and future prospects of AI-driven en

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Hogan, Robin J., Christopher A. T. Ferro, Ian T. Jolliffe, and David B. Stephenson. "Equitability Revisited: Why the “Equitable Threat Score” Is Not Equitable." Weather and Forecasting 25, no. 2 (2010): 710–26. http://dx.doi.org/10.1175/2009waf2222350.1.

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Abstract In the forecasting of binary events, verification measures that are “equitable” were defined by Gandin and Murphy to satisfy two requirements: 1) they award all random forecasting systems, including those that always issue the same forecast, the same expected score (typically zero), and 2) they are expressible as the linear weighted sum of the elements of the contingency table, where the weights are independent of the entries in the table, apart from the base rate. The authors demonstrate that the widely used “equitable threat score” (ETS), as well as numerous others, satisfies neithe

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Rozel, John Samuel. "Complexity, computational modeling, and forecasting the future of threat management." Journal of Threat Assessment and Management 6, no. 3-4 (2019): 193–97. http://dx.doi.org/10.1037/tam0000131.

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ROMANOVSKA, Yuliya. "ANALYSIS OF THE DEVELOPMENT OF THREATS TO THE SOCIO-ECONOMIC SYSTEM “CITY”." Herald of Khmelnytskyi National University. Economic sciences 304, no. 2(2) (2022): 111–18. http://dx.doi.org/10.31891/2307-5740-2022-304-2(2)-19.

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Consideration of a city socio-economic security in borders of a protective approach involves identifying threats to the “city” socio-economic system, studying their nature, sources of occurrence, identifying their character and forecasting the consequences of their implementation. There is a threat to the socio-economic system “city” considered in the paper as processes and phenomena occurring in the external and internal environment of the system, which, in the presence of a certain combination of conditions and circumstances in the system functioning, are capable to make negative changes in

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Samani, Nidhi, Komal Yadav, Yash Bhamare, Meghana Patil, and Dr P. P. Halkarnikar. "COVID-19 Future Forecasting using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2081–84. http://dx.doi.org/10.22214/ijraset.2022.42729.

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Abstract: Machine learning (ML) based forecasting mechanisms have proved their significance to anticipate in perioperative outcomes to improve the decision making on the future course of actions. The ML models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to ma

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Couce-Vieira, Aitor, David Rios Insua, and Alex Kosgodagan. "Assessing and Forecasting Cybersecurity Impacts." Decision Analysis 17, no. 4 (2020): 356–74. http://dx.doi.org/10.1287/deca.2020.0418.

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Cyberattacks constitute a major threat to most organizations. Beyond financial consequences, they may entail multiple impacts that need to be taken into account when making risk management decisions to allocate the required cybersecurity resources. Experts have traditionally focused on a technical perspective of the problem by considering impacts in relation with the confidentiality, integrity, and availability of information. We adopt a more comprehensive approach identifying a broader set of generic cybersecurity objectives, the corresponding set of attributes, and relevant forecasting and a

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ABDULRAHMAN, IBRAHIM ABDUL, UZOAMAKA C. OGOR, GABRIEL TOSIN AYODELE, CHIDOZIE ANADOZIE, and JACOB ALEBIOSU. "AI-Driven Threat Intelligence and Automated Incident Response: Enhancing Cyber Resilience through Predictive Analytics." Research Journal in Civil, Industrial and Mechanical Engineering 2, no. 1 (2025): 16–32. https://doi.org/10.61424/rjcime.v2i1.236.

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Cybersecurity is a critical concern for organizations as the complexity and volume of cyber threats continue to grow. Traditional methods of threat detection and incident response, such as signature-based detection and rule-based systems, are increasingly ineffective against sophisticated and evolving attacks. This study explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing threat intelligence and automating incident response. By leveraging predictive analytics, anomaly detection, and real-time data processing, AI-driven systems offer significant impro

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Sateesh Kumar Rongali and Durga Bramarambika Sailaja Varri. "AI in health care threat detection." World Journal of Advanced Research and Reviews 25, no. 3 (2025): 1784–89. https://doi.org/10.30574/wjarr.2025.25.3.0552.

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Medical detection and management through Artificial Intelligence (AI) constitutes a transformative healthcare force which identifies and handles health threats including infectious diseases combined with chronic conditions and new worldwide health challenges. Worldwide healthcare systems reveal extensive problems that the fast-evolving AI technologies encompassing ML, DL and NLP demonstrate ability to resolve. AI stands as a promising solution to minimize both health threats' mortality rates and morbidity through diagnostic process automation as well as surveillance capabilities improvement an

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KOTZERUBA, N.V. "Methodology for predicting the potential bankruptcy of an enterprise." Market Relations Development in Ukraine №9(256)2022 102 (December 15, 2022): 39–50. https://doi.org/10.5281/zenodo.7441787.

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The subject of the study is a set of methodical features of the implementation of the financial status and forecasting the threat of bankruptcy of the enterprise. The purpose of writing the article is to study the theoretical aspects of the analysis for the purpose of forecasting the bankruptcy of an enterprise based on the results of a decrease in its solvency. Methodology of the work – in the process of writing this article, general scientific and specific methods were used, in particular, economic and logical methods and methods of economic analysis; graphical and tabular methods were

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Elsenheimer, Cynthia B., and Chad M. Gravelle. "Introducing Lightning Threat Messaging Using the GOES-16 Day Cloud Phase Distinction RGB Composite." Weather and Forecasting 34, no. 5 (2019): 1587–600. http://dx.doi.org/10.1175/waf-d-19-0049.1.

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Abstract In 2001, the National Weather Service (NWS) began a Lightning Safety Awareness Campaign to reduce lightning-related fatalities in the United States. Although fatalities have decreased 41% since the campaign began, lightning still poses a significant threat to public safety as the majority of victims have little or no warning of cloud-to-ground lightning. This suggests it would be valuable to message the threat of lightning before it occurs, especially to NWS core partners that have the responsibility to protect large numbers of people. During the summer of 2018, a subset of forecaster

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Mykolaichuk, Mykola, Nina Petrukha, Liudmyla Akimova, Nataliia Pozniakovska, Bohdan Hudenko, and Oleksandr Akimov. "Conceptual principles of analysis and forecasting threats to national security in modern conditions." Sapienza: International Journal of Interdisciplinary Studies 6, no. 2 (2025): e25029. https://doi.org/10.51798/sijis.v6i2.985.

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This study critically examines the evolving landscape of national security threats, arguing that traditional response-oriented strategies are increasingly insufficient in the face of complex, multifaceted, and rapidly emerging dangers, including latent and non-conventional forms. Drawing on a comprehensive narrative literature review and adopting systemic, structural-functional, and phenomenological approaches with realist ontological assumptions, we analyze the dynamic interaction of geopolitical shifts, technological advancements, and the rise of "magic weapon" influence operations, particul

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Kulikovskij, Dmitriy O., and Daria N. Khalina. "Analysis creation process of a secure enterprise information system." Digital technology security, no. 4 (December 25, 2023): 35–46. http://dx.doi.org/10.17212/2782-2230-2023-4-35-46.

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The article is an analysis of the key stages of the development of a secure enterprise information system. The focus is on highlighting the stages of determining information security objectives, risk assessment, vulnerability identification, as well as the process of making informed decisions in the context of information system security. The article provides detailed recommendations for creating reports on the completed risk analysis and the process of making informed decisions based on the results obtained. As part of the work, the analysis of the processes of compiling a matrix of the proba

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PASHAJ, Klorenta, Vilma TOMÇO, and Eralda GJIKA. "From threat to response: Cybersecurity evolution in Albania." Smart Cities and Regional Development (SCRD) Journal 9, no. 1 (2025): 17–27. https://doi.org/10.25019/apwqc567.

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As Albania's digital landscape expands, its vulnerability to sophisticated cyber threats increases correspondingly. This study explores the evolution of Albania's cybersecurity strategies, tracing the shift from reactive measures to a proactive defence approach. It critically examines significant cyber incidents that have shaped the national cybersecurity landscape, assessing how these have influenced the development of Albania's current cyber defence strategies. The focus is on how Albania has tailored its strategic responses to enhance digital resilience and national security. In the second

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Sufi, Fahim. "A New Time Series Dataset for Cyber-Threat Correlation, Regression and Neural-Network-Based Forecasting." Information 15, no. 4 (2024): 199. http://dx.doi.org/10.3390/info15040199.

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In the face of escalating cyber threats that have contributed significantly to global economic losses, this study presents a comprehensive dataset capturing the multifaceted nature of cyber-attacks across 225 countries over a 14-month period from October 2022 to December 2023. The dataset, comprising 77,623 rows and 18 fields, provides a detailed chronology of cyber-attacks, categorized into eight critical dimensions: spam, ransomware, local infection, exploit, malicious mail, network attack, on-demand scan, and web threat. The dataset also includes ranking data, offering a comparative view of

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Siva, Krishna Jampani. "AI-Driven Threat Intelligence: Revolutionizing Proactive Cyber Defense." Journal of Scientific and Engineering Research 8, no. 6 (2021): 220–27. https://doi.org/10.5281/zenodo.14637382.

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Artificial Intelligence is revolutionizing cybersecurity, changing defensive strategies from reactive to proactive. This article has focused on AI-driven threat intelligence as the transformative force, detailing machine learning models that enable real-time data analysis, pattern recognition, and predictive analytics. AI's ability to detect anomalies and predict cyber threats before they materialize has surpassed traditional reactive measures. This proactive approach significantly enhances organizational resilience against constantly evolving threats. Innovations in anomaly detection and pred

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Pavel, Yermalovich, and Mejri Mohamed. "ONTOLOGY-BASED MODEL FOR SECURITY ASSESSMENT: PREDICTING CYBERATTACKS THROUGH THREAT ACTIVITY ANALYSIS." International Journal of Network Security & Its Applications (IJNSA) 12, no. 03 (2020): 01–22. https://doi.org/10.5281/zenodo.3887536.

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The prediction of attacks is essential for the prevention of potential risk. Therefore, risk forecasting contributes a lot to the optimization of the information security budget. This article focuses on the ontology and stages of a cyberattack. It introduces the main representatives of the attacking side and describes their motivation.

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Guo, Hongwu, Yongjie Ma, Zufeng Li, Qingzhi Zhao, and Yuan Zhai. "The Evaluation of Rainfall Forecasting in a Global Navigation Satellite System-Assisted Numerical Weather Prediction Model." Atmosphere 15, no. 8 (2024): 992. http://dx.doi.org/10.3390/atmos15080992.

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Accurate water vapor information is crucial for improving the quality of numerical weather forecasting. Previous studies have incorporated tropospheric water vapor data obtained from a global navigation satellite system (GNSS) into numerical weather models to enhance the accuracy and reliability of rainfall forecasts. However, research on evaluating forecast accuracy for different rainfall levels and the development of corresponding forecasting platforms is lacking. This study develops and establishes a rainfall forecasting platform supported by the GNSS-assisted weather research and forecasti

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Nicholson, Keith. "Intelligence led risk: Leveraging threat operations to deliver effective risk management." Cyber Security: A Peer-Reviewed Journal 8, no. 4 (2025): 306. https://doi.org/10.69554/qang4128.

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Cyberattacks pose a significant threat to organisations, presenting substantial business risks. Chief executive officers (CEOs) and boards must allocate investments to mitigate these risks effectively. Many leaders, however, struggle to incorporate cyber risk into their broader enterprise risk management strategies, often citing its technical complexity as a challenge. Yet, other risks — such as regulatory or financial — are also technical but do not face the same integration difficulties. This paper contends that cyber risk is challenging not due to its technical nature, but because it is non

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Miran, Seyed M., Chen Ling, Joseph J. James, and Lans Rothfusz. "Comparing Effectiveness of Four Graphical Designs for Probabilistic Hazard Information for Tornado Threat." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 2029–33. http://dx.doi.org/10.1177/1541931213601461.

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Finding effective measures to decrease the nation’s loss resulting from severe weather is crucial. Probabilistic Hazard Information (PHI) system is being developed for severe weather forecasting. Little work has been done to investigate which kind of graphical designs for PHI can increase users’ perception and interpretation, and elicit the best user response to the threats. This study investigates the effects of varying graphical designs for tornado threat. Four designs, “four colors”,” red scale”, “gray scale” and “contour” were tested. Radar was provided for one set of treatments, the other

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P, Sukumar, Sanjaikumar N, Ranjani M, Sandhiya G, and Sathish R. "COVID-19 Future Forecasting System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2209–10. http://dx.doi.org/10.22214/ijraset.2023.50536.

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Abstract: Humanity is in danger as a result of COVID-19’s global spread. Due to the illness’s high infectivity and transmissibility, some of the most potent economies in the world are taxed. The capacity of algorithms using expert systems to predict the number of upcoming COVID-19 patients who would contract the disease, which is now thought to pose a threat to humanity. In this study, COVID-19 risk factors were predicted using four traditional predictive models, including the minimal absolute contraction and operator for selection (LR, LASSO, SVM, ES, and LSTM). Each model offers projections

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Veerkar, Megha M., and Prof Meenaxi T. "Covid-19 Future Forecasting using Machine Learning and Deep Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40397.

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Forecasting mechanism used by Machine learning (ML) and deep learning model have proved their significance to predicting the in operative outcomes to enhance the choice making on the longer term course of actions. The ML models and deep learning models have long been utilized in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly wont to handle forecasting problems. This study demonstrates the potential of ML and Deep Learning models to forecast the amount of upcoming patients suffering from

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Brilly, M., and M. Polic. "Public perception of flood risks, flood forecasting and mitigation." Natural Hazards and Earth System Sciences 5, no. 3 (2005): 345–55. http://dx.doi.org/10.5194/nhess-5-345-2005.

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Abstract. A multidisciplinary and integrated approach to the flood mitigation decision making process should provide the best response of society in a flood hazard situation including preparation works and post hazard mitigation. In Slovenia, there is a great lack of data on social aspects and public response to flood mitigation measures and information management. In this paper, two studies of flood perception in the Slovenian town Celje are represented. During its history, Celje was often exposed to floods, the most recent serious floods being in 1990 and in 1998, with a hundred and fifty re

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Soloviev, Sergey, Mikhail Tarelkin, Vasily Tekunov, and Yuri Yazov. "Status and Prospects of Development Methodological Support for Technical Protection of Information in Information systems." Voprosy kiberbezopasnosti, no. 1(53) (2023): 41–57. http://dx.doi.org/10.21681/2311-3456-2023-1-41-57.

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The goal of article is determine the main areas for development, composition and structure of prospective methodological support for the organization and maintenance of technical protection of information in information systems.The method of research: is summary and analysis the existing methodological support for organization and maintenance of the technical protection of information from unauthorized access and its development trends in the interests of the conversion from qualitative to quantitative procedures of substantiation requirements and selection process to build information securit

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Zhuravka, Fedir, Yuriy Petrushenko, Svitlana Chorna, et al. "Financial security of Ukraine in debt burden: Assessment and forecasting." Public and Municipal Finance 14, no. 2 (2025): 111–29. https://doi.org/10.21511/pmf.14(2).2025.11.

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This study aims to comprehensively assess the state’s financial security of Ukraine in the context of mounting debt and economic instability. Particular attention is paid to the security of the financial sector, which determines the overall level of financial system security. This involves constructing a regression model of the relationship between the level of financial security and debt, the size of the economy, and reserves, as well as forecasting likely medium-term dynamics.Modeling revealed imbalances. During the war, the level of financial security dropped to a critical level, providing

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Antonescu, Bogdan, David M. Schultz, Alois Holzer, and Pieter Groenemeijer. "Tornadoes in Europe: An Underestimated Threat." Bulletin of the American Meteorological Society 98, no. 4 (2017): 713–28. http://dx.doi.org/10.1175/bams-d-16-0171.1.

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Abstract The social and economic impact of tornadoes in Europe is analyzed using tornado reports from the European Severe Weather Database between 1950 and 2015. Despite what is often assumed by the general public and even by meteorologists and researchers, tornadoes do occur in Europe and they are associated with injuries, fatalities, and damages, although their reported frequencies and intensities are lower compared with the United States. Currently, the threat of tornadoes to Europe is underestimated. Few European meteorological services have developed and maintained tornado databases and e

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Demuth, Julie L., Rebecca E. Morss, Isidora Jankov, et al. "Recommendations for Developing Useful and Usable Convection-Allowing Model Ensemble Information for NWS Forecasters." Weather and Forecasting 35, no. 4 (2020): 1381–406. http://dx.doi.org/10.1175/waf-d-19-0108.1.

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AbstractU.S. National Weather Service (NWS) forecasters assess and communicate hazardous weather risks, including the likelihood of a threat and its impacts. Convection-allowing model (CAM) ensembles offer potential to aid forecasting by depicting atmospheric outcomes, including associated uncertainties, at the refined space and time scales at which hazardous weather often occurs. Little is known, however, about what CAM ensemble information is needed to inform forecasting decisions. To address this knowledge gap, participant observations and semistructured interviews were conducted with NWS f

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Koren, Ore. "Means to an end: Pro-government militias as a predictive indicator of strategic mass killing." Conflict Management and Peace Science 34, no. 5 (2015): 461–84. http://dx.doi.org/10.1177/0738894215600385.

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Forecasting models of state-led mass killing are limited in their use of structural indicators, despite a large body of research that emphasizes the importance of agency and security repertoires in conditioning political violence. I seek to overcome these limitations by developing a theoretical and statistical framework that highlights the advantages of using pro-government militias (PGMs) as a predictive indicator in forecasting models of state-led mass killing. I argue that PGMs can lower the potential costs associated with mass killing for a regime faced with an internal threat, and might h

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Lehominova, Svitlana, and Halyna Haidur. "ANALYSIS OF CURRENT THREATS TO THE INFORMATION SECURITY OF ORGANIZATIONSAND THE FORMATION OF THE INFORMATION PLATFORM AGAINST THEM." Cybersecurity: Education, Science, Technique 2, no. 22 (2023): 54–67. http://dx.doi.org/10.28925/2663-4023.2023.22.5467.

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Taking into account the process of complication of the geopolitical and geoeconomic landscape space, the development of information technologies and the formation of new security challenges associated with the emergence of new cyber threats, there is a need for constant monitoring and forecasting of them in order to prevent consequences in the form of damage and leakage of valuable and confidential information. The authors analyzed the new predictable cyber security threats to organizations, with special attention paid to the protection of endpoints. Threats identified in the field of artifici

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Bista, Raghu Bir. "Forecasting Climate Variability in Nepal." SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education 12, no. 1 (2023): 8–20. http://dx.doi.org/10.35877/sainsmat1150.

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Climate variability is noted a critical issue in Nepal. This paper investigates empirically and analytically whether climate variability exists or not in different altitudes, whether its direction moves and what will be its future direction. In this study, we used a time series forecasting model based on the secondary data of hydrology and metrology collected from the Department of Hydrology and Metrology, the Government of Nepal. As a result, this paper found climate variability in the different parts of Nepal where variations of climatic variables (temperature and rainfall precipitation) are

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Baldwin, Michael E., and John S. Kain. "Sensitivity of Several Performance Measures to Displacement Error, Bias, and Event Frequency." Weather and Forecasting 21, no. 4 (2006): 636–48. http://dx.doi.org/10.1175/waf933.1.

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Abstract The sensitivity of various accuracy measures to displacement error, bias, and event frequency is analyzed for a simple hypothetical forecasting situation. Each measure is found to be sensitive to displacement error and bias, but probability of detection and threat score do not change as a function of event frequency. On the other hand, equitable threat score, true skill statistic, and odds ratio skill score behaved differently with changing event frequency. A newly devised measure, here called the bias-adjusted threat score, does not change with varying event frequency and is relative

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Murawski, Lech. "METHODOLOGY OF THREAT AUTONOMOUS ASSESSMENT AND FORECASTING THE LIFETIME OF THE MARINE STRUCTURE." Journal of KONES. Powertrain and Transport 22, no. 2 (2015): 163–68. http://dx.doi.org/10.5604/12314005.1165673.

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Condon, Andrew J., Y. Peter Sheng, and Vladimir A. Paramygin. "Toward High-Resolution, Rapid, Probabilistic Forecasting of the Inundation Threat from Landfalling Hurricanes." Monthly Weather Review 141, no. 4 (2013): 1304–23. http://dx.doi.org/10.1175/mwr-d-12-00149.1.

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Abstract State-of-the-art coupled hydrodynamic and wave models can predict the inundation threat from an approaching hurricane with high resolution and accuracy. However, these models are not highly efficient and often cannot be run sufficiently fast to provide results 2 h prior to advisory issuance within a 6-h forecast cycle. Therefore, to produce a timely inundation forecast, coarser grid models, without wave setup contributions, are typically used, which sacrifices resolution and physics. This paper introduces an efficient forecast method by applying a multidimensional interpolation techni

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Kidder, Stanley Q., John A. Knaff, Sheldon J. Kusselson, Michael Turk, Ralph R. Ferraro, and Robert J. Kuligowski. "The Tropical Rainfall Potential (TRaP) Technique. Part I: Description and Examples." Weather and Forecasting 20, no. 4 (2005): 456–64. http://dx.doi.org/10.1175/waf860.1.

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Abstract Inland flooding caused by heavy rainfall from landfalling tropical cyclones is a significant threat to life and property. The tropical rainfall potential (TRaP) technique, which couples satellite estimates of rain rate in tropical cyclones with track forecasts to produce a forecast of 24-h rainfall from a storm, was developed to better estimate the magnitude of this threat. This paper outlines the history of the TRaP technique, details its current algorithms, and offers examples of its use in forecasting. Part II of this paper covers verification of the technique.

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Bulgakova, L. S. "Safety Issues Environmental Security." Courier of Kutafin Moscow State Law University (MSAL)) 1, no. 11 (2023): 183–90. http://dx.doi.org/10.17803/2311-5998.2022.99.11.183-190.

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The article is devoted to the issues of environmental safety. The author reveals the content of threats to the environment, gives their classification. The importance of forecasting the possibility of the emergence of prospective and new threats is noted, the mechanisms of such forecasting are considered. The article proves that climate change is the most important threat to the security of the Russian Federation, and therefore their timely identification and assessment are among the priorities of climate policy. Analyzing the content of the National Security Concept of 2015 and the Environmen

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Bondarenko, Yevhen, Ivo Svoboda, Ivan Tkachov, Oleksandr Kozenko, and Volodymyr Vislovukh. "The impact of biometric technologies on the efficiency of terrorist crime investigation." Salud, Ciencia y Tecnología - Serie de Conferencias 4 (March 11, 2025): 1530. https://doi.org/10.56294/sctconf20251530.

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Introduction: The relevance of biometric technology in investigating terrorist crimes is increasing due to the rise in global security threats and the need to enhance law enforcement effectiveness. The main goal of this article is to evaluate how biometric technologies enhance the identification, tracking, and prevention of terrorist activities globally.Methods: The research methodology includes statistical data analysis, calculation of weighted averages, and scenario forecasting concerning terrorist attacks. The research methodology involves statistical analysis of terrorism-related data, wei

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Ali, Ali, and Mohd Khanapi Abd Ghani. "Forecasting for Vaccinated COVID-19 Cases using Supervised Machine Learning in Healthcare Sector." Journal of Intelligent Systems and Internet of Things 14, no. 2 (2025): 165–77. http://dx.doi.org/10.54216/jisiot.140214.

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Machine learning (ML)-based forecasting techniques have demonstrated significant value in predicting postoperative outcomes, aiding in improved decision-making for future tasks. ML algorithms have already been applied in various fields where identifying and ranking risk variables are essential. To address forecasting challenges, a wide range of predictive techniques is commonly employed. Research indicates that ML-based models can accurately predict the impact of COVID-19 on Jordan's healthcare system, a concern now recognized as a potential global health threat. Specifically, to determine COV

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Salpasaranis, Konstantinos, and Vasilios Stylianakis. "Forecasting Models of the Coronavirus (COVID-19) Cumulative Confirmed Cases Using a Hybrid Genetic Programming Method." European Journal of Engineering Research and Science 5, no. 12 (2020): 52–60. http://dx.doi.org/10.24018/ejers.2020.5.12.2129.

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The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encap

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Salpasaranis, Konstantinos, and Vasilios Stylianakis. "Forecasting Models of the Coronavirus (COVID-19) Cumulative Confirmed Cases Using a Hybrid Genetic Programming Method." European Journal of Engineering and Technology Research 5, no. 12 (2020): 52–60. http://dx.doi.org/10.24018/ejeng.2020.5.12.2129.

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The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encap

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