Rook et al., 2014 - Google Patents
A case study of applying data mining to sensor data for contextual requirements analysisRook et al., 2014
View PDF- Document ID
- 12127422604540449262
- Author
- Rook A
- Knauss A
- Damian D
- Thomo A
- Publication year
- Publication venue
- 2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)
External Links
Snippet
Determining the context situations specific to contextual requirements is challenging, particularly for environments that are largely unobservable by system designers (eg, dangerous system contexts of use and mobile applications). In this paper, we describe the …
- 238000007418 data mining 0 title abstract description 54
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
- G06K9/629—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/20—Image acquisition
- G06K9/22—Image acquisition using hand-held instruments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rožanec et al. | Human-centric artificial intelligence architecture for industry 5.0 applications | |
| Chinnaraju | Explainable AI (XAI) for trustworthy and transparent decision-making: A theoretical framework for AI interpretability | |
| Qiu et al. | Design of an energy‐efficient IoT device with optimized data management in sports person health monitoring application | |
| Harris et al. | Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts | |
| Yadav et al. | Crime prediction using auto regression techniques for time series data | |
| Moe et al. | Collaborative worker safety prediction mechanism using federated learning assisted edge intelligence in outdoor construction environment | |
| Bhatia et al. | Quantum computing inspired framework of student performance assessment in smart classroom | |
| Chen et al. | Future outdoor safety monitoring: Integrating human activity recognition with the internet of physical–virtual things | |
| Vieira et al. | Driftage: a multi-agent system framework for concept drift detection | |
| Garg et al. | A Multi-Layered AI-IoT Framework for Adaptive Financial Services | |
| Vimala et al. | Real-Time Smartphone Distraction Detection in Virtual Learning via Attention-CNN-LSTM | |
| Gilal et al. | A rule-based approach for discovering effectivesoftware team composition | |
| Rook et al. | A case study of applying data mining to sensor data for contextual requirements analysis | |
| Vianny et al. | Private AI: An Overview | |
| Roshankar et al. | Geocrime analytic framework (GCAF): A comprehensive framework for dynamic spatial temporal crime analysis | |
| Bárcena et al. | Federated learning of explainable artificial intelligence models for predicting Parkinson’s disease progression | |
| Mitcheltree et al. | Cyber security culture as a resilience-promoting factor for human-centered machine learning and zero-defect manufacturing environments | |
| Obinikpo et al. | Big data aggregation in the case of heterogeneity: a feasibility study for digital health | |
| Kumar et al. | Application of wrapper based hybrid system for classification of risk tolerance in the Indian mining industry | |
| Niu et al. | Terminal Forensics in Mobile Botnet Command and Control Detection Using a Novel Complex Picture Fuzzy CODAS Algorithm | |
| Neshenko et al. | A deep learning-based adaptive cyber disaster management framework | |
| Misra et al. | Human Factors and Security in Digital Twins: Challenges and Future Prospects | |
| Kumar et al. | Cyber ML-based cyberattack prediction framework in healthcare cyber-physical systems | |
| Joshua et al. | TinyML for Anomaly Detection | |
| Dcruz et al. | Structured AI decision-making in disaster management |