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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Robinson, Joseph N. Payan, Alexia P. Mavris, Dimitri N. Johnson, Charles Georgia Inst Technol Aerosp Syst Design Lab Sch Aerosp Engn 270 Ferst Dr Atlanta GA 30332 USA Georgia Inst Technol Adv Aerosp Anal Sch Aerosp Engn 270 Ferst Dr Atlanta GA 30332 USA FAA NextGen WJHTC Off Syst Safety Sect William J Hughes Tech CtrAviat Res Div Atlantic City NJ 08405 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents in the U.S. occur during either the approach or landing phases of flight and that unstabilized approaches are a leading cause due to the associated increased potential for loss of control, loss of situational awareness, or controlled flight into terrain. In the present work, routine flight data records obtained as part of a voluntary helicopter flight data monitoring program are used to identify, analyze, and evaluate approach events in a variety of helicopter flights from different mission types, such as emergency medical services, flight training, and research. A semi-automated process is developed which enables detection of the approach phase, construction of a nominal approach path, analysis of approach stability, and knowledge discovery through data mining. The process was able to identify 3950 visual and 183 instrument approaches in a set of 3749 operational helicopter flight data records. Analysis of visual approach events suggests that typical approaches are higher and steeper than expected. Thus, the traditional definition of a stabilized approach may not align with typical operations. Additionally, atypical approaches can be classified as highly stabilized, marginally unstabilized, or unstabilized depending on their associated stability measures.

关键词： Rotor Systems data Fusion Instrument Approach Procedure Helicopter Flight data Monitoring data mining algorithms Controlled Flight into Terrain data Analytics Flight Training Instrument Flight Rules Global Positioning System

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Enhancing Network Intrusion Detection Model Using Machine Learning algorithms

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Computers, Materials & Continua 2021年第4期67卷 979-990页

作者： Nancy Awadallah Awad Department of Computer and Information Systems Sadat Academy for Management SciencesCairo11742Egypt

After the digital revolution,large quantities of data have been generated with time through various *** networks have made the process of data analysis very difficult by detecting attacks using suitable *** Intrusion Detection Systems(IDSs)secure resources against threats,they still face challenges in improving detection accuracy,reducing false alarm rates,and detecting the unknown *** paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion *** experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion *** study focuses on several data mining algorithms such as;naïve Bayes,decision trees,support vector machines,decision tables,k-nearest neighbor algorithms,and artificial neural ***,this paper is concerned with the association process in creating attack rules to identify those in the network audit data,by utilizing a KDD99 dataset anomaly *** focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection *** implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy(0.992)and the lowest false positive rate(0.009).

关键词： Intrusion detection association rule data mining algorithms KDD99

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A new integrated data mining model to map spatial variation in the susceptibility of land to act as a source of aeolian dust

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第33期27卷 42022-42039页

作者： Gholami, Hamid Mohammadifar, Aliakbar Pourghasemi, Hamid Reza Collins, Adrian L. Univ Hormozgan Dept Nat Resources Engn Bandar Abbas Hormozgan Iran Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Rothamsted Res Sustainable Agr Sci Dept Okehampton EX20 2SB Devon England

This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.

关键词： Dust provenance susceptibility Integrated modelling Cluster analysis data mining algorithms

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Proposed S-Algo plus data mining algorithm for web platforms course content and usage evaluation

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SOFT COMPUTING 2020年第19期24卷 14861-14883页

作者： Kazanidis, Ioannis Valsamidis, Stavros Gounopoulos, Elias Kontogiannis, Sotirios Int Hellen Univ Adv Educ Technol & Mobile Applicat Lab Kavala Greece Int Hellen Univ Dept Accounting & Finance Kavala Greece TEI Western Macedonia Dept Business Adm Grevena Greece Univ Ioannina Dept Math Ioannina Greece

This paper suggests a novel data mining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S-Algo+ (Superposition Algorithm), takes as input the course rankings and the suggestion results from any kind of ranking/hierarchical algorithms and evaluates the validity of a course ranking position. The ranking algorithms estimate the quantity and quality of the course content according to users' actions and interest. S-Algo+ generates an improved final ranking suggestion output, combining the best results of the source ranking algorithms using statistical and mathematic techniques. In this way, the researchers and course instructors can use more accurate results. The efficiency and applicability of the S-Algo+ algorithm was evaluated successfully with a cross-comparison quantitative and qualitative process in a case study at a Greek university. Our new proposed S-Algo+ algorithm may lead to both theoretical and practical advantages. It may also apply not only for course evaluation but for any kind of web application such as e-commerce.

关键词： data mining algorithms Web mining in education e-Learning course ranking Course knowledge discovery and management Learning analytics

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Toward an improved learning process: the relevance of ethnicity to data mining prediction of students' performance

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SN APPLIED SCIENCES 2020年第1期2卷 8-8页

作者： Adekitan, Aderibigbe Israel Salau, Odunayo Tech Univ Ilmenau Dept Elect Engn & Informat Technol Ilmenau Germany Covenant Univ Dept Business Management Ota Ogun State Nigeria

The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions toward preventing failure and dropout tendencies. Students are generally admitted based on their evaluated academic potentials as measured using their admission criteria scores. This study seeks to identify the relationship, if any, between the admission criteria scores and the graduation grades, and to examine the influence of ethnicity using the geopolitical zone of origin of the student on the predictive accuracy of the models developed using a Nigerian University as a case study. data mining analyses were carried out using four classifiers on the Orange Software, and the results were verified with multiple regression analysis. The maximum classification accuracy observed is 53.2% which indicates that the pre-admission scores alone are insufficient for predicting the graduation result of students but it may serve as a useful guide. By applying over-sampling technique, the accuracy increased to 79.8%. The results establish that the ethnic background of the student is statistically insignificant in predicting their graduation results. Hence, the use of ethnicity in admission processes is therefore not ideal.

关键词： Educational data mining Nigerian university data mining algorithms Performance evaluation methodologies Knowledge discovery Machine learning

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Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing

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Neuroscience Informatics 2025年第2期5卷 100194-100194页

作者： Banchhor, Chitrakant Rawat, Manoj Kumar Joshi, Rahul Dhabliya, Dharmesh Kulkarni, Omkaresh Pande, Sandeep Dwarkanath Pawar, Umesh Computer Science & Engg. (AI) Department Vishwakarma Institute of Information Technology Pune India Medicaps university Indore India CSE Department Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology Vishwakarma Institute of Technology Maharashtra Pune India Computer Science & Engg. (AIML) Department Vishwakarma Institute of Information Technology Pune India School of Computer Engineering MIT Academy of Engineering Alandi Maharashtra Pune India School of Computer Science and Engineering Sandip University MS Nashik India

The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems. © 2025 The Author(s)

关键词： data mining algorithms English learning Legacy problems Multiple intelligences theory

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Critical Components Identification for Cyber-Physical Power Systems Considering Time-Varying Operational States 9

Critical Components Identification for Cyber-Physical Power ...

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9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)

作者： Liu, Yigu Semertzis, Ioannis Stefanov, Alexandru Palensky, Peter Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands

ISBN: (纸本)9781450386081

The security issues of Cyber-Physical power Systems (CPS) have attracted widespread attention from scholars. Vulnerability assessment emerges as an effective method to identify the critical components and thus increase the system resilience. While efforts have been made to study the vulnerability features of power systems under the occurrence of a single, discrete disturbance or failure at a specific time instant, this paper focuses on identifying the critical components of the cyber-physical system considering time-varying operational states. To investigate the potentially ever-changing CPS vulnerability features, in this paper we construct a database of cascading failure chains using quasi-dynamic simulations to capture the vulnerability relationships among components under time-varying operational states. Then, by adopting sequential mining algorithms, we mine the most frequent cascading failure patterns and identify the critical components based on the data mining results. Simulation studies are conducted on IEEE 39-bus and IEEE RTS-96 systems to evaluate the effectiveness of the proposed method for the identification of critical components at both cyber and physical layers.

关键词： cyber-physical systems vulnerability assessment data mining algorithms

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Parental stress in the relationship with the child and personality traits that parents shape in their children

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EARLY CHILD DEVELOPMENT AND CARE 2021年第2期191卷 198-209页

作者： Szymanska, Agnieszka Aranowska, Elzbieta Cardinal Stefan Wyszynski Univ Inst Psychol Ul Woycickiego 1-3 Bud 14 PL-01938 Warsaw Poland

The aim of the research was to determine what relations occur between the stress experienced by parents in the relationship with their children and the personality traits that they shape in them. Personal characteristics that parents shape in their children have been classified into metatraits described in the Circumplex of Personality model. The study was carried out on a sample of 319 parents of preschool children. Two data mining methods were used: text mining algorithms and cluster analysis carried out by data mining algorithms. Parents experiencing higher stress more often shaped Self-restraint (Delta Plus) personality features. They tried to ensure that their children did not develop Sensation-Seeking (Delta Minus) features associated with impulsivity and dominance over others. Parents experiencing lower parental stress developed Stability (Alpha Plus) qualities related to social adaptation in their children and tried to ensure that they did not develop the antisocial features of Disinhibition (Alpha Minus).

关键词： Parental stress parental goals development of personality traits data mining algorithms

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Bio-Inspired data mining for Optimizing GPCR Function Identification

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2021年第4期15卷 1页

作者： Bekhouche, Safia Ben Ali, Yamina Mohamed Badji Mokhtar Univ Lab Rech Informat LRI Annaba Algeria

GPCRs are the largest family of cell surface receptors;many of them remain orphans. The GPCR functions prediction represents a very important bioinformatics task. It consists in assigning to the protein the corresponding functional class. This classification step requires a good protein representation method and a robust classification algorithm. However, the complexity of this task could be increased because of the great number of GPCRs features in most databases, which produce combinatorial explosion. In order to reduce complexity and optimize classification, the authors propose to use bio-inspired metaheuristics for both the feature selection and the choice of the best couple (feature extraction strategy [FES], data mining algorithm [DMA]). The authors propose to use the BAT algorithm for extracting the pertinent features and the genetic algorithm to choose the best couple. They compared the results they obtained with two existing algorithms. Experimental results indicate the efficiency of the proposed system.

关键词： BAT Algorithm Classification data mining algorithms Feature Extraction Strategies Feature Selection Function Prediction Genetic Algorithm GPCR

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An New Algorithm-based Rough Set for Selecting Clustering Attribute in Categorical data 6

An New Algorithm-based Rough Set for Selecting Clustering At...

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6th International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Baroud, Muftah Mohamed Jomah Hashim, Siti Zaiton Mohd Zainal, Anazida Ahnad, Jamilah Univ Teknol Sch Comp 28 UTM Skudai Johor Bahru Skudai Malaysia Univ Teknol Fac Social Scince & Humanities UTM Skudai Johor Bahru Skudai Malaysia

ISBN: (纸本)9781728151977;9781728151960

Several algorithms strategies based on Rough Set Theory (RST) have been used for the selection of attributes and grouping objects that show similar features. On the other hand, most of these clustering techniques cannot deal tackle partitioning. In addition, these processes are computationally complexity and low purity. In this study, the researcher looked at the limitations of the two rough set based techniques used, Information-Theoretic Dependency Roughness (ITDR) and Maximum Indiscernible Attribute (MIA). They also proposed a novel method for selecting clustering attributes, Maximum mean Attribute (MMA). They compared the performance of MMA, ITDR and MIA technique, using UCI and benchmark datasets. Their results validated the performance of the MMA with regards to its purity and computational complexity.

关键词： Knowledge discovery data mining Rough set theory data mining algorithms Performance Attribute selection Attribute dependency Information system Clustering Categorical data

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