Robust Bioinformatics Recognition with VLSI Biochip Microsystem

A VLSI bio-inspired microsystem for real-time, on-site, robust image analysis and recognition has been developed. A corresponding novel artificial neural network (ANN) learning algorithm using new sigmoid-logarithmic transfer function is invented. Our results show the trained new ANN can recognize low fluorescence patterns better than the conventional sigmoidal ANN does. A differential logarithmic imaging chip is designed for calculating logarithm of relative intensities of fluorescence signals. The single-rail logarithmic circuit and a prototype ANN chip are designed, fabricated and characterized.

The use of renewable energy sources in the production of electricity has become inevitable in order to reduce the greenhouse gases left in the atmosphere that cause the Earth to warm up. Although countries on a national basis have implemented a number of policies to support electricity generated from renewable energy sources, investments to produce electricity without a license on a local basis are not desirable. According to the climatic conditions of the power plant of 1 MW installed founded in Konya and power plant production data are monitored. Machine learning is a sub-branch of artificial intelligence that deals with the design and development of algorithms that allow computers to develop their behavior based on experimental data. In this study, Naive Bayes, Decision Tree, CN2 Rule Induction, Random Forest, Support Vector Machine, k-Nearest Neighbor, Artificial Neural Network, Logistic Regression and AdaBoost machine learning algorithms are used for prediction and classification. Generally, energy investors are curious about the return on their investment. It is very important for energy providers to predict how much electricity will be generated from existing solar power plants and accordingly determine the measures they will take to meet the electricity demand in the future. ROC analyzes were performed for machine learning models and performance evaluation was performed. In this study, the best performance estimation value obtained from the solar power plant depending on the weather conditions was obtained with 92.24% accuracy.

A hybrid machine learning and embedded IoT-based water quality monitoring system

The electricity demand is increasing day by day due to industrial growth and the rise in the living standards of human beings in Kadoma, Zimbabwe. Electricity generation cannot only be dependent on fossil fuels because of carbon dioxide emissions to the atmosphere, which causes global warming and its devastating effects. In the context of distributed generation, renewable energies (RE)-based Microgrids (MGs) could be sourced to meet the electricity demand. However, the unpredictable nature of RE resources may pose a significant risk of unavailable and/or unreliable electricity supply. This paper proposes optimal scheduling by making use of Internet of Things (IoT) devices in the MG. The MG system comprised a solar farm, wind farm, battery storage system (BSS), diesel generator, and a residential load. The proposed decision-making algorithm was developed using python and was implemented to improve scheduling in the MG system. Additionally, a comparison between three machine learning algorithms (Artificial Neural Network, Random Forest and Extreme Gradient Boosting) was implemented to determine the superior algorithm when it comes to accurately predict the sources to give power at a certain time to satisfy the load. The results indicated that the availability of electricity was enhanced by the use of IoTs in the microgrid. For accuracy prediction, the Extreme Gradient Boosting machine learning algorithm outperformed the Artificial Neural Network and Random Forest machine learning algorithms.

The Levenberg-Marquardt and Newton are two algorithms that use the Hessian for the artificial neural network learning. In this article, we propose a modified Levenberg-Marquardt algorithm for the artificial neural network learning containing the training and testing stages. The modified Levenberg-Marquardt algorithm is based on the Levenberg-Marquardt and Newton algorithms but with the following two differences to assure the error stability and weights boundedness: 1) there is a singularity point in the learning rates of the Levenberg-Marquardt and Newton algorithms, while there is not a singularity point in the learning rate of the modified Levenberg-Marquardt algorithm and 2) the Levenberg-Marquardt and Newton algorithms have three different learning rates, while the modified Levenberg-Marquardt algorithm only has one learning rate. The error stability and weights boundedness of the modified Levenberg-Marquardt algorithm are assured based on the Lyapunov technique. We compare the artificial neural network learning with the modified Levenberg-Marquardt, Levenberg-Marquardt, Newton, and stable gradient algorithms for the learning of the electric and brain signals data set.

A compact integrated system-on-chip (SoC) architecture solution for robust, real-time, and on-site genetic analysis and biomarker recognition has been developed. This microsystem solution is noise tolerable and suitable for analyzing the weak fluorescence patterns from a PCR prepared dual-labeled DNA microchip assay. In the architecture, a preceding VLSI differential logarithm microchip is designed for effectively computing the logarithm of the normalized input fluorescence signals. A posterior VLSI artificial neural network (ANN) processor chip is used for analyzing the processed signals from the differential logarithm stage. A single-channel logarithmic circuit was fabricated and characterized. A prototype ANN chip with unsupervised winner-take-all (WTA) function was designed, fabricated, and tested. An ANN learning algorithm using a novel sigmoid-logarithmic transfer function based on the supervised backpropagation (BP) algorithm is proposed for robustly recognizing low intensity patterns. Our results show the trained new ANN can recognize low fluorescence patterns better than an ANN using the conventional sigmoid function.

This paper presents the development of several efficient learning algorithms for neural networks (ELEANNE). The ELEANNE 1 and ELEANNE 2 are two recursive least-squares learning algorithms, proposed for training single-layered neural networks with analog output. This paper also proposes a new optimization strategy for training single-layered neural networks, which provides the basis for the development of a variety of efficient learning algorithms. This optimization strategy is the source of the ELEANNE 3, a second-order learning algorithm for training single-layered neural networks with binary output. A simplified version of this algorithm, called ELEANNE 4, is also derived on the basis of some simplifying but reasonable assumptions. The two algorithms developed for single-layered neural networks provide the basis for the derivation of ELEANNE 5 and ELEANNE 6, which are proposed for training multilayered neural networks with binary output. The ELEANNE 7 is an efficient algorithm developed for training multilayered neural networks with either binary or analog output. >

Key aspects of the categorical perception of initial stop consonants by human and animal listeners are replicated by a ‘‘synthetic listener’’ in the form of a computational auditory model. This model is a composite of a physiologically faithful ‘‘front end’’ [M. J. Pont and R. I. Damper, J. Acoust. Soc. Am. 89, 1213–1228 (1991)] and an artificial neural network (ANN) ‘‘back end.’’ ANNs are trained on neurograms (i.e., the front end’s auditory-nerve representation) for the 0- and 80-ms end points of three voice-onset time continua (bilabial, alveolar, and velar), and tested on the intermediate (10, 20, ..., 70 ms) patterns. Replication of the behavioral data—in terms of warping of similarity space and shift of phoneme boundary with place of articulation—is robust in that it is maintained across a variety of ANN architectures and learning algorithms. Unlike real listeners, the synthetic listener can be analyzed easily and manipulated purposefully to reveal the acoustic and auditory features underlying the voicing contrast. A contribution analysis of the weights and activations of the ANNs highlights the auditory features. Replacement of the Pont–Damper front end by a simple Fourier-based spectral analysis abolishes the boundary-shift effect, although similarity warping is maintained. This suggests that restructuring of the acoustic features by the peripheral auditory system is crucial to phonetic classification.

The paper presents a back propagation based artificial neural network learning algorithm for recognizing human faces. We have worked on eight features for recognition. A facial recognition system has been proposed to recognize registered faces in the database & new faces that are not part of the database. The basic objective is to understand the ability of back propagation learning algorithms for face recognition task. Few experimental observations have also been provided.

Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks

For many years, artificial neural networks (ANNs) have been studied and used to model information processing systems based on or inspired by biological neural structures. They not only can provide solutions with improved performance when compared with traditional problem-solving methods, but also give a deeper understanding of human cognitive abilities. Among various existing neural network architectures and learning algorithms, Kohonen’s selforganizing map (SOM) [46] is one of the most popular neural network models. Developed for an associative memory model, it is an unsupervised learning algorithm with a simple structure and computational form, and is motivated by the retina-cortex mapping. Self-organization in general is a fundamental pattern recognition process, in which intrinsic interand intra-pattern relationships among the stimuli and responses are learnt without the presence of a potentially biased or subjective external influence. The SOM can provide topologically preserved mapping from input to output spaces. Although the computational form of the SOM is very simple, numerous researchers have already examined the algorithm and many of its problems, nevertheless research in this area goes deeper and deeper – there are still many aspects to be exploited.

The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.

Fractional calculus has been adopted in the modelling of many scientific processes and systems. Due to the inherent feature of long term memory of fractional derivatives, it has been used in the learning process of neural networks. A fractional order derivative based back propagation learning algorithm in neural networks is proposed in this paper. Specifically, Riemann-Liouville (R-L), Caputo (C) and Caputo Fabrizio (CF) fractional Derivative based on the back propagation algorithms in a three layer feed-forward neural network employed. To get a faster learning rate without oscillation, momentum factor is incorporated. The effect of fractional order and momentum factor is investigated and compared. The performance of these fractional derivatives based algorithms with integer derivatives based algorithm in terms of mean square error (MSE), particularly the salary based on years of experience is predicted. Results demonstrate that fractional derivative based learning algorithms outperform the integer derivatives.

At present, deep learning is becoming one of the most popular approach to creation of the artificial intelligences systems such as speech recognition, natural language processing, computer vision and so on. The paper presents a historical overview of deep learning in neural networks. The model of the artificial neural network is described as well as the learning algorithms for neural networks including the error backpropagation algorithm, which is used to train deep neural networks. The development of neural networks architectures is presented including neocognitron, autoencoders, convolutional neural networks, restricted Boltzmann machine, deep belief networks, long short-term memory, gated recurrent neural networks, and residual networks. Training deep neural networks with many hidden layers is impeded by the vanishing gradient problem. The paper describes the approaches to solve this problem that provide the ability to train neural networks with more than hundred layers. An overview of popular deep learning libraries is presented. Nowadays, for computer vision tasks convolutional neural networks are utilized, while for sequence processing, including natural language processing, recurrent networks are preferred solution, primarily long short-term memory networks and gated recurrent neural networks.

Optimal learning in artificial neural networks: A review of theoretical results