Top Research Papers on Data Structures and Algorithms PDF

This work investigates the issue of algorithms’ data hungriness, presents a comprehensive review of existing data-efficient methods and systematizes them into four categories, and delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.

A novel DDEA with two efficient components, a boosting strategy for self-aware model managements and a localized data generation method to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions.

Experimental analysis indicates that the proposed work is more successful in terms of attack resistance, scalability, execution speed and accuracy when compared with other algorithms that are used for privacy preservation.

This work conducts an empirical investigation of Last.fm, an online music discovery platform, and finds that data mining and data management techniques are increasingly permeate organizations and the contexts in which they are embedded.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.

Various machine learning techniques and models that use time series prediction are introduced in this paper including ARIMA, Kalman filtering, Holt winters'Exponential smoothing, Random walk, KNN Algorithm, and Deep Learning.

Recognizing how model design impacts harm opens up new mitigation techniques that are less burdensome than comprehensive data collection.

This paper proposes a method to improve the AdaBoost algorithm using the new weighted vote parameters for the weak classifiers using the basis of the global error rate and the classification accuracy rate of the positive class, which is the primary interest.

The time series forecasting and the Bayesian inference, in combination with the genetic algorithms, can prove to be powerful artificial intelligence tools.

Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The f...

Big data has increasingly become predominant in many research fields affecting human knowledge, including medicine and engineering. Cluster analysis, or clustering, is widely recognized as one of the most effective processes to deal with various types of data, especially big data. There has been considerable interest in Fuzzy C-Means (FCM) as a method for clustering data using a short-distance approach in data mining. However, despite its simplicity, this method is not suitable for clustering large data sets due to their complex structure. In particular, FCM is sensitive to cluster center init...

This paper proposes a novel metaheuristic called CryStAl, chiefly inspired by the principles underlying the formation of crystal structures from the addition of the basis to the lattice points, which is a natural phenomenon that can be seen in the symmetric arrangement of constituents in crystalline minerals such as quartz.

The basic theory of two SIM algorithms, namely, optical sectioning SIM (OS-SIM) and superresolution SIM (SR-SIM), are introduced, and their implementation modalities are summarized.

This paper proposes a new public data repository for benchmarking stream algorithms with real-world data that contains the most popular datasets from literature and new datasets related to a highly relevant public health problem that involves the recognition of disease vector insects using optical sensors.

The pattern of garbage-coded deaths in the world is identified and the methods used to determine their redistribution to generate more plausible cause of death assignments are presented to represent an overall improvement in empiricism compared to past reliance on a priori knowledge.

Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.

This article portrays the data-ization of online fandom in China, arguing that the traffic data has been dematerialized as new affective object in fan–object relations, while digital fan culture has been constructed into a type of algorithmic culture.

Experimental results analysis show that the proposed model, CryptoGA, is robust and provides better performance on selected parameters as compared to state-of-the-art cryptographic algorithms i.e. DES, 3DES, RSA, Blowfish, and AES.

An improved SMOTE algorithm based on Normal distribution is proposed in this paper, so that the new sample points are distributed closer to the center of the minority sample with a higher probability to avoid the marginalization of the expanded data.

The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs and only requires about 2% computational budgets to produce competitive results.