A comprehensive survey on support vector machine classification: Applications, challenges and trends

New updated! The latest book from a very famous author finally comes out. Book of handbook of individual differences learning and instruction, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

/pdf/handbook-of-individual-differences-learning-and-instruction-3z4xw1epjn.pdf

Handbook of individual differences, learning, and instruction.

There has been a recent surge of success in utilizing Deep Learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.

Optimizing deep learning hyper-parameters through an evolutionary algorithm

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.

Big Data Meet Green Challenges: Big Data Toward Green Applications

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.

Speaker identification features extraction methods: A systematic review

The success of Deep Neural Networks (DNNs) for various applications like language processing (NLP), image processing, character recognition inspired to use machine learning (ML) and Evolutionary Computation (EC) techniques for improving learning process. Using evolutionary algorithms to improve the efficiency of deep learning attained some success. However, these techniques are unable to reduce the learning time which is the key concern for deep learning. The main problem with DNN is that, it uses a random topology to start with (similar to artificial neural networks). If the topology is not suitable, training procedure will restart with a new topology and this process continues till expected results are obtained. In this paper, we propose, for the first time, a new prospect for evolving optimized deep neural networks which can provide a warm start to the training process compared to heuristic random initial architecture. We discuss the theoretical approach towards possibility of optimizing the learning process inspired from the existing un-conventional approaches. The training process of DNN with EC approach is faster than regular approach by a considerable difference of over 6 hours for MNIST data set. Further, we observed a considerable improvement in the classification accuracies. Our approaches resulted in an improved classification accuracy of 2% and 4.4%for MNIST data set and 1.2% and 1.4% for IRIS data set compared to heuristic random weights approach. Our initial experimental results prove that evolutionary approaches provides a warm start to the deep learning, thus, reducing the training time.

Evolving deep neural networks: A new prospect

Deep learning is becoming an increasingly interesting and powerful machine learning method with successful applications in many domains, such as natural language processing, image recognition, and hand-written character recognition. Despite of its eminent success, limitations of traditional learning approach may still prevent deep learning from achieving a wide range of realistic learning tasks. Due to the flexibility and proven effectiveness of evolutionary learning techniques, they may therefore play a crucial role towards unleashing the full potential of deep learning in practice. Unfortunately, many researchers with a strong background on evolutionary computation are not fully aware of the stateof-the-art research on deep learning. To close this knowledge gap and to promote the research on evolutionary inspired deep learning techniques, this paper presents a comprehensive review of the latest deep architectures and surveys important evolutionary algorithms that can potentially be explored for training these deep architectures. Index terms — Deep Architectures, Deep Learning, Evolutionary Algorithms

/pdf/implementation-of-evolutionary-algorithms-for-deep-3qlwpqw1x0.pdf

Implementation of evolutionary algorithms for deep architectures

Deep learning (DL) is becoming an increasingly interesting and powerful machine learning method with successful applications in many domains, such as natural language processing, image recognition, hand-written character recognition, and computer vision. Despite of its eminent success, limitations of traditional learning approach may still prevent deep learning from achieving a wide range of realistic learning tasks. DL approaches has shown success in speech recognition and speaker identification over traditional approaches such as those that use Mel Frequency Cepstrum Coefficients for feature extraction with Gaussian Mixture Models. However, speaker identification research community are not fully aware of the DL process and its application with respect to speaker identification. This paper is motivated to reduce this knowledge gap and to promote the research of implementing deep learning techniques for speaker identification. In this paper, we present a review of the DL methodologies used for speaker identification and surveys important DL algorithms that can potentially be explored for future works. We categorised the applications of DL for speaker identification according to the process of speaker identification and presented a review of these implementations.

A review on Deep Learning approaches in Speaker Identification

There has been an exponential increase in the usage of the internet, particularly among students since the introduction of e-learning and Bring Your Own Device (BYO) initiatives into the education system. In New Zealand the percentage of the population using the internet is now 93.8% and this increase in internet usage has increased the risk of cybersecurity attacks. This makes it necessary to provide awareness and education on cybersecurity to students who are potential targets for exploitation. However, to provide this awareness it is necessary to understand what their current knowledge on cybersecurity is which forms the basis of this paper. This paper presents the results of a survey conducted on internet usage and cybersecurity awareness among three age groups between 8 years and 21 years. A questionnaire consisting of various questions on internet usage and cybersecurity concepts was prepared. For this survey, we considered both computers (desktops & laptops) and mobile devices (tablets & smartphones). The results of the survey showed that cybersecurity awareness among the surveyed students was generally low with the lowest level in the 8–12-year age group. The students of 8–12 age group were able to answer only 19% of survey questions. Furthermore, most of the students were not familiar with common cybersecurity terms and did not demonstrate enough awareness of common threats such as phishing. The results further show that the majority of the students were not aware of cybersecurity tools for tablets and smartphones which are frequently used devices for BYOD. The key contribution of this paper is to emphasise the necessity to create cybersecurity awareness among students.

/pdf/a-survey-on-internet-usage-and-cybersecurity-awareness-in-hmoyfwgrfi.pdf

Sreenivas Sremath Tirumala

Papers

Speaker identification features extraction methods: A systematic review

Evolving deep neural networks: A new prospect

Implementation of evolutionary algorithms for deep architectures

A review on Deep Learning approaches in Speaker Identification

A survey on internet usage and cybersecurity awareness in students