Artifical Neural Networks

This narrative describes the deeper root of bio-viral weaponization and consciousness warfare in distant human history with the negative aliens. It is explicated that this form of nefarious strive to create genetic mutation and consciousness-bioenergetic reversal goes as far back as humanity was first thriving as a 12D crystalline solar organic consciousness being in the Nebadon Universe, which most likely goes back as far back as 550 million years ago, and only 10 million years after its embodiment to experience physical experiential sojourns within the first 9 dimensions of this universe. In fact, the main problem started after the Lyran Wars, when the negative aliens having constructed a black hole system through making rips in the space-time and joining the resulting wormholes to one another, later began to engage in wars culminating in blowing up the 12 th universal stargate that was Lyra. Beyond, this, with the failure of the Metatronic Collective to attempt to put the pieces of the Lyran grid back together after having fallen into the Milky Way Galaxy and in pursuit of repairing the stargate system and the universal 12D grid and its timeline structure, it led to the advent of 'Metatronic Reversal' through a 'Fibonacci form of consciousness progression sequence' and the advent of the 'Yahweh matrix,' and what is referred to as the 'Caduceus Network,' whose symbol our medical industry today ignorantly boasts to display as its plaque representing healthcare and medicine. Thus, as they say, "the rabbit hole is much deeper as it seems," as the parasitic negative aliens have constantly used these technologies to invade our Lightbody and nadial system, not to only consistently siphon our precious divine energies to feed themselves and their nefarious AI machinery, but to inflict 'death seals' upon humanity, culminating in humanity undergoing a series of futile cycles of birth and physical death through repetitive reincarnations, confining us within a timeline loop that has been seemingly endless. And, the advent of this current, purportedly pandemic bio-viral infection referred to as the "Coronavirus," epitomizes yet another one of their ploys in bio-viral weaponization and warfare, with the delusional intent to, once for all, make a fully docile race out of humans as automatons subjecting us to their AI machinery or whims, through what is now to be followed by a transhumanistic-hybridization movement, using electromagnetic energies and radiation poisoning together with the aid of nanotechnology, devising yet another retrovirus against us. However, the time of their atrocities and crimes committed against humanity and its consciousness is now over, with the advent of a group of benevolent extradimensional and transdimensional and much higher frequential consciousness beings helping us to finally collect up our fragmented soul pieces in order to be restored to our original 12D DNA strand crystalline genetics, that we refer to as full Ascension.

Previous studies have shown that in platelets of mild Alzheimer Disease (AD) patients there are alterations of specific APP forms, paralleled by alteration in expression level of both ADAM 10 and BACE when compared to control subjects. Due to the poor linear relation among each key-element of beta-amyloid cascade and the target diagnosis, the use of systems able to afford non linear tasks, like artificial neural networks (ANNs), should allow a better discriminating capacity in comparison with classical statistics.

Industrial control systems are nowadays exposed in environments with rapid and unstable parameter changes and uses measuring equipments with critical output sensitivity. In the case of thermal gas analyzer, measurement errors are contributed by temperature, gas flow, and pressure. Error compensation is a key problem for these control systems. In recent years, it has been proven in the literature that artificial neural network (ANN) is a reliable and low cost solution to manage errors. Among all the algorithms of ANN, the back propagation is commonly used because of its simplicity and learning methodology is easy to realize. However, it has two notable drawbacks: (a) it is likely to run into local minimum, and (b) convergence is slow. Thermal conductivity gas analyzer often works in adverse surroundings, which requires fast and accurate measurements. Therefore, a strong learning network is needed. This paper proposes a novel thermal gas analyzer using adaptive neuro-fuzzy inference system. The effectiveness and validity of the proposed method is verified by simulation studies using MATLAB. Fuzzy membership rules are created to allow regulation of learning parameters. Further, the fuzzy adaptive network model is constructed to train large data samples while the high precision compensation of sensor error is realized by the improved flow. Simulation results reveal that the convergence speed and output accuracy is improved and the learning parameters in thermal gas analyzer are automatically corrected by the proposed method in comparison with the back propagation algorithm of artificial neural network.

Speech recognition techniques are one of the most important modern technologies. Many different systems have been developed in terms of methods used in the extraction of features and methods of classification. Voice recognition includes two areas: speech recognition and speaker recognition, where the research is confined to the field of speech recognition.

Based on my PhD thesis published in 2003, the research reported in this work treats natural language as a physical system, subject to the same forces as other systems describable in physical terms and uses a computational approach to demonstrate.  The language of mechanical transformations, such as first-order phase transitions, is directly applicable to fundamental structural changes which occur during the spread of any given human natural language. 

While the research focus on the features emergent from the exchange dynamics of language evolution in human populations, it also highlights that similar features are mirrored within the language acquisition processes of individual human brains, from infant to adulthood. As such, language can be viewed as a window into neural functional activities which, in turn, can be described as a mechanical system. The study of evolution of natural language can thus be used to understand the dynamics of brain evolution and perhaps the emergence of what we like to call "consciousness".

In nature, first-order phase transitions are not rare, and often give rise to noticeable transformative phenomena, such as water turning into ice, when temperatures reach a specific low. As heat is removed from an H2O system, less energy is shared between molecules. In turn, molecules resist giving up heat, which require much energy. When heat is not longer available, they then settle into a low energy state, in this case, a lattice, in a dramatic display of crystallization.

The acquisition of language, as it spreads through a population, show striking similarities to heat distribution through a molecular or atomic system. Language is an embodied system which require our relation to the world to generate neural effects when we hear the use of words and sentences. As language spreads through a population the use of individual words become less relatable to specific physical experiences and thus become more vague. Through this loss of "meaning" bits of languages undergo structural transformations that affect the whole use of language much like removing heat from a physical system.

Urmia Lake is the 20st largest lake in the world and the second salt lake of the World according to its high salinity. This lake is considered as an evacuation place and final destination of some important rivers of northwest of Iran. This lake is also very important because of its effects on economic and environment of region. In recent years because of decrease of rainfall and drought, unessential use of agricultural water, evaporation on water surface of lake, bracing of water surface flows by dams in upstream of rivers and digging of wells near the lake, water surface level of lake has decreased and salinity of water of the lake has increased very much. Considering the importance of water surface level and salinity of lake, study on these items through environmental approach play important role for decision making. It is necessary to predict water level and salinity of Urmia Lake for future programming to prevent, continue of this critical situation. In this research water level and salinity of Urmia Lake have been analyzed and predicted by using artificial neural networks and statistical software. Obtained results from this research showed that water level of Urmia Lake is predicted very well. Also prediction of salinity has shown acceptable results. Calculation of water level and salinity of this lake in a same time prediction mode is another research that has been investigated in this study.

This work presents the development of a proof-of-concept generative design tool for the AEC industry, named Aplysia. Aplysia has the capability to provide the designer with the ability to produce emergent design solutions from multiobjective criteria without the tradeoff between number of objectives and computational resources. The current inefficiencies with existing generative design tools are primarily due to the underlying algorithms, such as evolutionary algorithms which require significant computational resources due to large search spaces, and inconsistencies between industry requirements and provided features, such as requiring domain expert input to use these tools which make it inaccessible to many users. We present the novel use of a compositional pattern-producing network (CPPN) and the Neuro-Evolution of Augmenting Topologies (NEAT) algorithm for a building-scale structure. This paper details the software development methodology to build the tool, driven by a user-centric approach. Requirements gathering, which framed the scope of Aplysia, was completed through a use case study. The user and technical requirements were translated into a distributed system architecture and user-friendly GUI. Aplysia was experimentally tested for the design of a lightweight, freestanding canopy. Our initial findings show that Aplysia has the potential to improve the generative design workflow for the test case, which we argue is more adaptable to real-world AEC design problems and outline further improvements in the continual development of Aplysia.

The theory of dual numerical means of random and experienced variables is briefly described in the framework of the new theory of experience and the chance that arises as an axiomatic synthesis of two dual theories — the Kolmogorov theory of probability and the theory of believability. A new term is introduced for the numerical mean of the experienced variable — mathematical reflection, which is dual to the mathematical expectation of a random variable within the framework of the new theory. The basic properties and examples of dual numerical means are considered.

In the world, energy demand continues to grow incessantly. At the same time, there is a growing need to reduce CO 2 emissions, greenhouse effects and pollution in our cities. A viable solution consists in producing energy by exploiting renewable sources, such as solar energy. However, for the efficient use of this energy, accurate estimation methods are needed. Indeed, applications like Demand/Response require prediction tools to estimate the generation profiles of renewable energy sources. This paper presents an innovative methodology for short-term (e.g. 15 minutes) forecasting of Global Horizontal Solar Irradiance (GHI). The proposed methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of solar radiation samples collected for four years by a real weather station. Then GHI forecast, the output of the neural network, is given as input to our Photovoltaic simulator to predict energy production in short-term time periods. Finally, experimental results for both GHI forecast and Photovoltaic energy prediction are presented and discussed.

With the advancement of wireless communication, optical wireless communication (OWC) has evolved into a communication method with a wide range of applications. However, different nonlinear effects in optical wireless communication will cause significant signal processing issues and degrade the system's performance. When paired with the visible light of machine learning methods, machine learning has a lot of potential for tackling nonlinear problems. Communication technology must be extremely valuable in terms of research. Traditional machine learning techniques like KGmeans, DBSCAN, and support vector machines (SVM) have been proven to perform well in pre-equalization, post-equalization, anti-system jitter, and phase correction in studies. The aforementioned methodologies are discussed and introduced, as well as their applications in the field of optical wireless communication signal processing, in the hopes of providing a reference for machine learning to handle numerous nonlinear problems in optical wireless communication. Because of its high nonlinear fitting capabilities, the deep neural network (DNN) can help the OWC system operate even better.

Data Warehouse is becoming more and more popular by providing business an edge over their competitors. DW is developed by using DWP methodology. Currently, there are large numbers of methodologies followed in market. The reason for this is the lack of any centralized attempts of creating platform-independent DWP standards. For developing a centralized DWP it is important to understand the existing methodologies. The authors have done a comparison of five well known methodologies and highlighted the similarities between the processes they used. A new DW architecture has been proposed by integrating those similar processes. This new architecture concentrates mainly on the quality of the DW, as it is one of the critical aspects of DW. Quality is being introduced by defining 3 new components named as quality control, DW monitor, DW Integration Change Management. Evolution of metadata and DW repository are the most important tasks of the quality management.

Yapay zekâ genel bir tanımı ile bir bilgisayar bilimidir. Bu bilim içerisinde, bilgisayarlar belirli algoritma yapılarının da yardımı ile, sistematik ve dinamik yapılar oluşturarak yeni veri ve yeni işlemler üretebilmektedir. Bu sebeple yapay zekâ, insan zekasının işlevselliğine benzetilmeye çalışılmıştır. Bunun nedeni, bilgisayarların da insan zekâsı gibi, hatırlamayı, düşünmeyi, işlemeyi, değerlendirmeyi ve karar vermeyi yapabiliyor olmasının hedeflenmesidir.  Bütün bu bilgiler ışığında, yapay zekanın asıl görev ve amacı, insan beyni gibi düşünebilme ve karar verebilme olaylarının sistematik bir yöntem ile gerçekleştiriliyor olmasıdır.

In the area of flights delay, most of the research done concentrate on developing flight schedules without
studying the real reasons for flights delay. The flights delay causes great loss in money and in travelers for the
airline companies. In this study, a feed forward multi-layer perceptron (MLP) with two hidden layers type of
artificial neural network (ANN) is built to determine the most air flight delay causes, predict the flights delay time
and prioritize the factors that affect the flights delay. Most of the delay causes found in this study need good
management to minimize the delay time.

Keywords: Flights delay, ANN, MLP, Classification, Prediction.

Behavioural Science is the study of human behaviour in different contexts, situation and time. Investigating about past human behaviour can help us calculate human behaviour in the future. In this paper we are analysing the public opinion of a product, as available on social media sites specifically Twitter. Our end goal is to visually represent the vital business insights that cannot be gathered from a plain dataset that can assist in developing further intelligent solutions. Sentence sentiment classification is a predictive modelling task achieved through supervised learning. Here, the extracted sentence is segregated into two target variables i.e. positive and negative stances using Natural Language Processing (NLP), through the utilization of neural networks. Categorization of the public sentiment will help in market testing, public anticipation of the product and public sentiment analysis. Market testing involves assessing the risks involved, gathering the bias people cradle and determining our prospective customers. Once the product is launched it is essential to understand how people judge the product. This will provide a platform for developers to improve in the future. As text is a sequence of information and not simply a discrete representation we need an iterative process to train the model, thus the application of RNN. For training the model, we mined our own dataset from twitter API so that the model got accustomed to the natural trend of writing, as in tweets. Using specific keywords, we gathered all posts and tweets related to our product. After data collection and cleaning, the polarity of sentences is found. A special kind of neural network called as the convolution LSTM-RNN is used to train the machine. Live social media data is given as an input to the model. Data is processed and distributed in its respective class. This result is stored in MSSQL server to which the Tableau's dashboard is connected. Using Tableau, we perform visual analytics on the collected data, where we can classify tweets geographically to understand location wise reaction. Having gathered this data a company might reach out to dissatisfied customers with solution to their predicament, thereby improving customer relation. Not only can we gather the notion people hold about a product but also about competing products.

Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain. Online transactions, shopping, and e-commerce are expanding step by step. Due to which in the online payment system, fraudulent activities have also increased. It has cost banks and their customers a loss of billions of rupees. The techniques used now a day detects the anomaly only after the fraud transaction takes place. The intruders have found ways to crack the system loopholes and defeat the security. These frauds are not consistent in their actions, they constantly alter. Thus, Artificial Intelligent (AI) algorithms are used to detect the behavior of such activity by learning the past behavior of the transaction of the users. An unsupervised algorithm is used to detect online transactions, as fraudsters commit fraud once by online media and then move on to other techniques. This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network. In this paper accuracy, precision, recall, and AUC curve are considered as a model evaluation factor.

This thesis encapsulates a study conducted to use Automatic Speech Recognition (ASR) techniques to identify dog barks from ambient recordings. To achieve this, four different classifiers are developed. The first of which relies on cross-correlation, the second on Vector Quantisation (VQ) and the third on an Artificial Neural Network (ANN). The output of each classifier is fed into a fourth ‘Jury’ classifier which determines the overall identification decision.

Mel Frequency Cepstral Coefficients (MFCCs) are used extract features from each recording. The extraction process is optimised by using 30 mel filters spread from 0 to 14 KHz and 15 cepstral coefficients alongside delta features.

The VQ classifier performed the most inconsistently, with little robustness to its applied parameters. Furthermore, its performance degraded considerably when additive white Gaussian noise was introduced in tests. The ANN displayed consistent and robust results.

Of the 537 analysed instances used to test the classifiers, 493 were correctly identified with 22 false positives and 22 false negatives. A second trial was conducted on unseen data in which 61 of the 73 recordings were correctly identified. In both cases a sensitivity of over 89% and specificity of 77% was achieved.

The study successfully concludes that human ASR techniques apply extremely well, and that the use of combining the output of multiple classifiers bolsters performance.

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

Bu yazımda yapay zekanın ne olduğu ve nerden doğduğuna
değinip; yapay zekâ teknolojisinin geleceği hakkındaki senaryolardan özellikle hukuk alanına olan etkilerinden bahsedeceğim. Tabi yazının ucu bize de dokunuyor. Bu yazıyı okuyan birçok kişi açısından geçerli olduğu üzere benim de bildiğim tek iş avukatlık ve öğretim üyeliği; yani geçimimi profesyonel hukuk hizmetlerinden sağlıyorum. Dolayısıyla bu gelişmeler ve tartışmalar ışığında şunu soruyorum: Mesleğimiz ve geçim kaynağımız elimizden gidiyor mu?

Scope of the book:
This book focusses on the technical concepts of deep learning and its associated branch Neural Networks for the various dimensions of image processing applications. The proposed volume intends to bring together researchers to report the latest results or progress in the development of the above-mentioned areas. Since there is a deficit of books on this specific subject matter, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings.
Topics of Interest:
This book solicits contributions, which include the fundamentals in the field of Deep Artificial Neural Networks and Image Processing supported by case studies and practical examples. Each chapter is expected to be self-contained and to cover an in-depth analysis of real life applications of neural networks to image analysis.

DNNs (Deep Neural Networks) are widely employed in advanced applications including image and audio processing. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DNNs that have been popular for industrial applications in recent years. RNNs are well-suited to time variation problems due to their recursive structure. RNNs are ideally suited for temporal variation concerns due to their recursive structure, whereas CNNs are often employed in computer vision applications such as object recognition. Despite the fact that CNNs and RNNs are both DNNs, their implementation differs significantly. Recurrent Neural Networks (RNN) can be used to solve the sequence to sequence problem when both the input and output have sequential structures. There are always some unseen links between the structures. The traditional RNN technique, on the other hand, has trouble examining the links between the sequences appropriately. This survey introduces some attentionbased RNN models, applications, types, and DNN algorithms that can focus on different features of the input for each output item in order to investigate and exploit the implicit links between the input and output items.

Coagulation is an essential part of drinking water treatment process. It is used to agglomerate suspended solids together into larger bodies. Chemical coagulants are added to water to facilitate bonding among particulates that are widely used to enhanced the removal particulate, colloidal and dissolved substances. Optimizing coagulation for selecting the best coagulation condition and coagulation dosage is important. Because excessive coagulant overdosing leads to increased treatment cost and public health concerns. Optimum coagulant dosage in water treatment is generally evaluated by jar test method which is used experimentally to determine the optimal conditions for the coagulation. Thus, jar test have limitations in that they are time consuming, intermittent, and are subject to variations in operator’s observations. As a result jar test is often overlooked. Furthermore, conducting to often jar testing will consume a lot of chemicals for testing, and also require an experienced to obtain good results in determining the required coagulant dosage.

In this study, the data is used in this work are collect real operational data from Drinking Water Treatment Plant in Istanbul, Turkey. Artificial neural network (ANN) is used to estimate non-linear relationships between inputs and outputs in a dataset. Multilayer ANN is contract to predicttreated water quality parameters and the optimal coagulant dosage. Several architectures with one hidden layer and hidden neurons are built to obtain the right and proper ANN model.

ANN can be used to overcome these limitations and coagulation control. With the use of ANN, jar test limitation is eliminated and also coagulant optimization is done very fast, efficient and removal of uncertainty before the water is supplied to the public.

ABSTRCT The existing neural networks activation functions, the Sigmoid among others is the only set uses across various applications of NNs such as microscopy, neuromorphic, optical, robotics, finance and transportation. Only one set applies to different areas of application. Also, these activation functions' selections are based on trial and error, neither emanate from the AI and or training datasets, nor from the testing data. This formed NNs' Black-box. Jamilu (2019) proposed that strong links between the AI and or training datasets and activation functions must be established. This is to replace the NNs' Black-box models with the models rely much less on experts' assumptions, and much more on input AI and or training datasets, time change and specific area of application. Thus, Jamilu (2019) proposed Criterion(s) for the rational selection of activation functions. The paper is to use superintelligent NNs for stock price predictions, portfolio optimization, and general application approaches to shed light on the paper' title.

Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.

The key to the keys to immortality and eternal youth lies in the correct answer to the main question: How to naively discover new essential – but still hidden – features required for properly training novel adaptive supervised machine learning algorithms (SMLA)

Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.

A new approach for object detection is presented. Prior work on object detection repurposes classifiers to perform detection. Instead, framing object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Proposed model processes images in real-time at 45 frames per second. It has been defined as a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications.

Recent proliferation of tunnels in urban congested areas necessitates the continual updating of subsidence prediction techniques using settlement records of tunnel monitoring programs. This paper concerns with characterization of the surface settlement trough associated with soft ground tunneling through a new model based on the Artificial Neural Networks (ANN). The key element of the employed paradigm is the novel combined topology of the information processing system to cover the different sizes of the learning database. The neural network has been trained using the monitoring and geotechnical data of many tunneling projects in Egypt and abroad. The training database covers a wide spectrum of construction techniques, geotechnical data and the tunnel geometrical data. The main benefit of this approach is to avoid the computational complexities of satisfying all the constraints of constructional details, geotechnical conditions and tunnel configurations in order to obtain a rigorous assessment of settlements associated with tunneling. The proposed model was practiced in analyzing the present and future status of Hydroshield tunneling in Greater Cairo.

An important element of a complex recycling process that is an integral part of municipal waste management is the sorting of materials that can be re-used. Manual sorting of garbage is a tedious and expensive process, which is why scientists create and study automated sorting techniques to improve the overall efficiency of the recycling process. An important aspect here is the preliminary division of waste into various groups, from which detailed segregation of materials will take place. One of the most important contemporary environmental problems is the recycling and utilization of plastic waste. The main problem under consideration in this article is the design of an automatic waste segregation system. A deep convoluted neural network will be used to classify images.

Multilabel Image Tagging is one of the most important challenges in computer vision with many real world applications and thus we have used Deep Neural Networks for Image Annotation to boost performance. This experiment is performed on NUS-WIDE Dataset with 1K Tags. I. INTRODUCTION Multilabel image annotation is an important and challenging problem in computer vision. Most existing work focus on single-label classification problems, where each image is assumed to have only one class label. However, this is not necessarily true for real world applications, as an image may be associated with multiple tags. As a practical example, images from Flickr are used as an example which has multiple tags, such as objects, activities, and scene descriptions. Images on the Internet, in general, are usually associated with sentences or descriptions, instead of a single class label, which is a type of multitagging. Therefore, it is a practical and important problem to accurately assign multiple labels to one image. Single-label image classification has been extensively studied in the vision community, the most recent advances reported on the large-scale ImageNet Architecture. Most existing work focus on designing visual features for improving recognition accuracy. For example, sparse coding, Fisher vectors, and VLAD have been proposed to reduce the quantization error of " bag of words "-type features. Very recently, deep convolutional neural networks (CNN) have demonstrated promising results for single-label image classification. Such algorithms have focused on one vs all, but one have worked on the multilabel image annotation problem. In this work, we used the highly expressive convolutional network for the problem of multilabel image annotation. We employed a similar network structure to as used in Image Net, which contains several convolutional and dense connected layers as the basic architecture. We studied and compared several other popular multilabel losses, such as the ranking loss that optimizes the area under ROC curve (AUC), and the cross-entropy loss used in Tagprop. Specifically, we propose to use the top-k ranking loss, inspired by, for embedding to train the network. Using the largest publicly available multilabel dataset NUS-WIDE, we observe a significant performance boost over conventional features, reporting the best retrieval performance. We even performed test on Triplet Loss giving much better results. II. MOTIVATION Multilabel image annotation is an important and challenging problem in computer vision. Most existing work focus on single-label classification problems, where each image is assumed to have only one class label. However, this is not necessarily true for real world applications, as an image may be associated with multiple tags. As a practical example, images from Flickr are used as an example which has multiple tags, such as objects, activities, and scene descriptions. Images on the Internet, in general, are usually associated with sentences or descriptions, instead of a single class label, which is a type of multitagging. Therefore, it is a practical and important problem to accurately assign multiple labels to one image.

Stock market price index prediction is a challenging task for investors and scholars. Artificial neural networks have been widely employed to predict financial stock market levels thanks to their ability to model nonlinear functions. The accuracy of backpropagation neural networks trained with different heuristic and numerical algorithms is measured for comparison purpose. It is found that numerical algorithm outperform heuristic techniques.

— Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as aneural network. Use neural network in forecasts time series can be agood solution, but the problem is network architecture and the training method in the right direction. General Regression Neural Network (GRNN) is one of the network model radial basis that used to approach a function. GRNN including model neural network model with a solution that quickly, because it is not needed each iteration in the estimation weight. This model has a network architecture that wasa number of units in pattern layer in accordance with the number of input data. One of the application GRNN is to predict the crude oil by using a model GRNN.From the training and testing on the data obtained by the RMSE testing 1.9355 and RMSE training 1.1048.Model is good to be used to give aprediction that is quite accurate information that is shown by the close target with the output

Extraction of maximum energy from wind and transferring it to the grid with high efficiency are challenging problems. To this end, this study proposes a smart pitch controller for a wind turbine-doubly fed induction generator system using a Differential Evolution (DE) based adaptive neural network. The nominal weights for the back-propagation neural network controller are obtained from input-output training data generated by DE optimization method. These weights are then adaptively updated in time domain depending on the variation of the system outputs. The adaptive control strategy has been tested through simulation of complete system dynamics comprising of the turbine-generator system and its various components. It has been observed that the DE based smart pitch controller is able to achieve efficient energy transfer to the grid and at the same time provide a good damping profile. Locally collected wind data was used in the testing phase.

Credit card fraud detection is growing due to the increase and the popularity of online banking. The need to detect fraudulent within credit card has become as a serious problem among the online shoppers. The multi-layer perceptron (MLP) machine learning algorithm is used to identify the credit card fraud. We have used the various parameters of the MLP to compare the performance of MLP. The aim of this paper is to design a high performance model to detect the credit card fraud using deep learning techniques. We found that logistic and hyperbolic tangent activation function offer good performance in detecting the credit card fraud. The logistic activation function performs better when there are 10 nodes, the sensitivity is 82% and when there are 100 nodes, the sensitivity is 83% respectively in the 3 hidden layer model. However, hyperbolic tangent activation function performs better when there is 1000 nodes, the sensitivity is 82% in all the number (1, 2 and 3) of hidden layers. This...

The field of automatic music composition has seen great progress in the last few years, much of which can be attributed to advances in deep neural networks. There are numerous studies that present different strategies for generating sheet music from scratch. The inclusion of high-level musical characteristics (e.g., perceived emotional qualities), however, as conditions for controlling the generation output remains a challenge. In this paper, we present a novel approach for calculating the valence (the positivity or negativity of the perceived emotion) of a chord progression within a lead sheet, using pre-defined mood tags proposed by music experts. Based on this approach, we propose a novel strategy for conditional lead sheet generation that allows us to steer the music generation in terms of valence, phrasing, and time signature. Our approach is similar to a Neural Machine Translation (NMT) problem, as we include high-level conditions in the encoder part of the sequence-to-sequence architectures used (i.e., long-short term memory networks, and a Transformer network). We conducted experiments to thoroughly analyze these two architectures. The results show that the proposed strategy is able to generate lead sheets in a controllable manner, resulting in distributions of musical attributes similar to those of the training dataset. We also verified through a subjective listening test that our approach is effective in controlling the valence of a generated chord progression.

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method...

The existing choices of Activation Functions of a deep learning neural network are majorly based on personal human judgments, biases, experiences and little quantitative skills, thus, neither generated from the training data, testing data nor emanated from the referenced AI-MI-Purified Data Set. In my previous paper, Jameel's ANNAF Stochastic Criterion and Lemma for selecting stochastic activation functions were proposed, however, the objective of this paper is to propose Definite Rules, not Trial and Error called "Jameel's ANNAF Deterministic Criterion and Lemma" for the choice of advanced optimized Activation Functions. This is the only paper that first applied "proposed Jameel's ANNAF Deterministic Criterion" to proposed about Two-Thousand Two-Hundred and Twenty Four, 2224 Advanced Activation Functions (mostly Deterministic) EMANATED from our AI SAMPLE DATA for the successful conduct of Deep Learning Artificial Neural Network. THREE out of which were Rated excellent Activation Functions for "Temperature vs Conductance" Deep Learning Artificial Neural Network. However, one can still find more candidates out of the remaining 2221 Activation Functions using the proposed criterion. The bottom line is that Advanced Deep Learning Artificial Neural Networks would depend on AI DATA, TIME CHANGE and the AREA OF APPLICATION.

Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically overestimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance. Citation: Tig ˘ demir M (2014) Re-Evaluation of the AASHTO-Flexible Pavement Design Equation with Neural Network Modeling. PLoS ONE 9(11): e113226.