We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

/pdf/a-comprehensive-survey-on-particle-swarm-optimization-1d380v5xx1.pdf

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

https://escholarship.org/content/qt5dk0b8t5/qt5dk0b8t5.pdf?t=q9piey

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining

Benchmark for filter methods for feature selection in high-dimensional classification data

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

/pdf/deep-learning-a-comprehensive-overview-on-techniques-1wh2eij623.pdf

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.

A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology

Abstract Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.

/pdf/a-review-of-feature-selection-and-its-methods-172ufuz4e8.pdf

A Review of Feature Selection and Its Methods

Network Security and Types of Attacks in Network

Particle swarm optimization is an evolutionary algorithm that depicts the movement of flock of birds in space in mathematical terms. In PSO we view each potential problem as a particle with certain velocity flying through a problem space just like a flock of bird. In the world of infinite data, the ability to handle imprecise and inconsistent information and to select important features from the data has become the important requirement of feature selection. PSO has come a long way since its beginning and has become an important tool for feature selection in numerous physical problems. In this paper we present a literary review of papers on PSO and chart its journey through inception and with implementing it in various physical problems. We will present a comparative table of implementation for PSO and review the success of PSO in various fields of science. This paper provides an incentive for the readers to join the PSO world.

A Survey on Particle Swarm Optimization in Feature Selection

The liver supports almost every organ in the body and is vital for our survival. Liver disease may not cause any symptoms at earlier stage or the symptoms may be vague, like weakness and loss of energy. Symptoms partly depend on the type and the extent of liver disease. Liver diseases are diagnosed based on the liver functional test. Though this disease cannot be predicted at earlier stage due to lack of symptoms and signs, in this paper we attempt to apply soft computing technique for intelligent diagnosis of liver disease. The classification and its type detection are implemented in three phases. In first phase, ANN classification is applied for classifying the liver disease. In second phase rough set rule induction using LEM algorithm is applied to generate classification rules. This rule induction overcomes the drawback of MLP and hence improves the accuracy. in third phase fuzzy rules are applied to identify the types of the liver disease. Using LEM algorithm 6 rules are generated with accuracy of 96% in correct classification. On applying rules generated by LEM, improves the classification accuracy by 6% compared to MLP. The 4 fuzzy rules are framed to indentify the types of liver disease.

J. Anuradha

Papers

A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology

A Review of Feature Selection and Its Methods

Network Security and Types of Attacks in Network

A Survey on Particle Swarm Optimization in Feature Selection

Classification and Rule Extraction using Rough Set for Diagnosis of Liver Disease and its Types