A5084771214- Data Mining Algorithms and Applications
- Data Management and Algorithms
- Advanced Database Systems and Queries
- Topic Modeling
- Natural Language Processing Techniques
- Web Data Mining and Analysis
- Time Series Analysis and Forecasting
- Hate Speech and Cyberbullying Detection
- Rough Sets and Fuzzy Logic
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Authorship Attribution and Profiling
- Advanced Chemical Sensor Technologies
- Algorithms and Data Compression
- Image and Signal Denoising Methods
- Advanced Image Fusion Techniques
- IoT and Edge/Fog Computing
- Advanced Battery Technologies Research
- Linguistics, Language Diversity, and Identity
- Power Systems and Renewable Energy
- Fuzzy Logic and Control Systems
- Food Supply Chain Traceability
- Computational Physics and Python Applications
- Energy Harvesting in Wireless Networks
- Swearing, Euphemism, Multilingualism
University of Aizu
2024
Jawaharlal Nehru Technological University Anantapur
2023
Sri Balaji Vidyapeeth University
2021
Amrita Vishwa Vidyapeetham
2016-2018
Social media text is generally informal and noisy but sometimes tends to have informative content. Extracting these content such as entities a challenging task. The main aim of this paper extract from Malayalam social efficiently. corpus used in our system FIRE2015 entity extraction This data initially subjected pre-processing feature then proceeds with extraction. Apart the conventional stylometric features like prefixes, suffixes, hash tags etc., POS tags, unsupervised word embedding...
Social media platforms are now widely used by the people to express their opinion or interest. The language users in social earlier was purely English. Code-mixed text, i.e., mixing of two more languages, is commonly seen now. Incode-mixed data, one will be written using another script. So process such code-mixed identification each word important for processing. main objective work propose a technique identifying Hindi-English data three namely, Facebook, Twitter, and WhatsApp....
Individuals utilize online networking sites like Facebook and Twitter to express their interests, opinions or reviews. The users used English language as medium for communication in earlier days. Despite the fact that content can be written Unicode characters now, people find it easier communicate by mixing two more languages together lean toward writing native Roman script. These types of data are called code-mixed text. While processing such social-media data, recognizing text is an...
Background/Objectives: Noise in a digital image, is unwanted information that degrades the quality of an image. The main aim proposed method to denoise noisy image based on least square approach using wavelet filters. Methods/ Statistical Analysis: One dimensional by Selesnick extended two denoising. In our technique problem formulation for denoising, matrix constructed second order filter coefficients replaced coefficients. Findings: experimented standard images namely Lena, Cameraman,...
Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing miss interesting partial a database. With this motivation, paper proposes novel model finding may exist temporal An efficient pattern-growth algorithm, called Partial Pattern-growth (3P-growth), also presented, which can effectively all desired within Substantial...
A geo-referenced time series database represents the data generated by a set of fixed locations (or items) observing particular phenomenon over time. Useful information that can facilitate users to achieve socio-economic development lies hidden in this data. This paper introduces novel model Fuzzy Geo-referenced Periodic-Frequent Patterns (FGPFPs) may exist these databases. An FGPFP frequently occurring neighboring items observed at regular intervals database. For example, an traffic...
Finding fuzzy frequent patterns in a quantitative database is challenging problem of significant importance many real-world applications. Past studies focused on mining these transactional databases by disregarding the spatiotemporal characteristics an item database. This paper proposes generic model spatial pattern (FFSP) that may exist Discovering FFSPs nontrivial and due to its huge search space high computational cost. A novel pruning technique, called neighborhood pruning, has been...
Partial periodic pattern mining is an important model in data with many real-world applications. However, this model's successful industrial application was hindered by the problem of combinatorial explosion patterns, which involves generating too most might be redundant or uninteresting to user. Furthermore, increases memory, runtime, and energy requirements a algorithm. This paper aims tackle challenging proposing novel maximal partial that may exist database. We also present new tree...
Spatial High Utility Itemset Mining (SHUIM) is an important knowledge discovery technique with many real-world applications. It involves discovering all itemsets that satisfy the user-specified m inimum u tility (minUtil) i n a q uantitative spatiotemporal database. The popular adoption and successful industrial application of this have been hindered by following two limitations: (i) Since rationale SHUIM to find minUtil constraint, it often produces too patterns, most which may be redundant...
Partial periodic-frequent pattern mining is an important knowledge discovery technique in data mining. It involves identifying all frequent patterns that have exhibited partial periodic behavior a temporal database. The following two limitations hindered the successful industrial application of this technique: (i) there exists no algorithm to find desired columnar databases, and (ii) existing algorithms are computationally expensive both terms runtime memory consumption. This paper tackles...
Periodic-frequent patterns are a vital class of regularities in temporal database. Most previous studies followed the approach finding these by storing occurrence information pattern list. While this facilitates existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) dense databases due increased list sizes. A renowned concept set theory is larger set, smaller its complement will be. Based conceptual fact, paper explores...
Partial periodic patterns are an important class of regularities in multiple time series data. Most previous works focused on finding these a binary by disregarding the quantities objects. This paper explores concept "fuzzy sets" and proposes novel model fuzzy partial (F3Ps) that may exist quantitative series. F3Ps have value because they represent predictable Unfortunately, is challenging due to its colossal search space. We introduce pruning technique reduce space computational cost...