multilayer perceptron neural network

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers.

1,2,3Student, Dept. of Computer Science Engineering, MIT School of Engineering, Maharashtra, India 4Professor, Dept. of Computer Science Engineering, MIT School of Engineering, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Farming is a significant occupation in India. It is the primary and major contributor to the Indian economy. Normally, about 70% of the families and 10% of the urban areas are dependent on agriculture. These numbers are decreasing and one of the major reasons is that the farmers are not generating enough revenue to sustain themselves let alone their farms. Farmers are not generating revenue because they are unaware of the economic factors. Also, at times improper planning results in lesser revenue, for example, harvesting at the time when there is a lesser demand. Hence it becomes very important for the farmer to analyze the economic factors wh...

In this study, to detect attacks of WSNs, a Hybrid Incursion Identification Approach (HIIA) is proposed. To reduce the amount of Energy Consumption (EC) of the sensor nodes, the HIIA mechanism utilizes a cluster-oriented approach with the LEACH protocol. For misuse observation and anomaly recognition, with MPNN (Multilayer Perceptron Neural Network) depended on fuzzy rule sets, HIIA structure is utilized. To refer to various varieties of attackers and to harmonize the identification results, with appendicle NN, FFNN (Feed Forward Neural Network) is utilized, that means Sybil Attack (SA), Hello Flood Attack (HFA) and Wormhole Attack (WA). To detect a SA, Improved SA Algorithm developed. Similarly, to detect a WA, that particular method is developed by Wormhole Anti-Hybrid Technique. Using the distance and power of the signal, HFA is detected. An exploratory research is conveyed out in a group of nodes. The nodes that misbehave in them are all determined. This proposed method, detects...

Mobile Ad hoc Networks (MANET) are the collection of mobile devices (nodes) which on a shared wireless medium communicate with each other even in the absence of predefined infrastructure. Security in MANETs is a challenging issue. The characteristics of this networks are such that they are inevitable to the attacks, one of such attack is Sybil attack where an attacker subverts a reputation system by creating forge identities, in this paper we are studying about Sybil attacks and its various forms alongside this we are also detailing about the solutions proposed or implemented to detect or prevent such attacks.

Spoken words recognition provides applications like spoken commands recognitions in robotics command, speech based number dialing for phones and mobiles, etc. It also provides applications in railway and banking areas. This work aims at designing of optimal Multilayer Perceptron Neural Network (MLP NN) based classifiers for speaker dependent spoken digits recognition. The classifier attempted as optimal leading to less number of computations and few components requirement for its future implementation in hardware leading to a low cost speech recognition system. Isolated spoken digits were used as an input data to the neural networks based classifiers. Each spoken word was analyzed for the feature like Mel Frequency Cepstral Coefficients (MFCC). The MLP NN based classifier was designed meticulously with

Spoken words recognition provides applications like spoken commands recognitions in robotics command, speech based number dialing for phones and mobiles, etc. It also provides applications in railway and banking areas. This work aims at designing of optimal Multilayer Perceptron Neural Network (MLP NN) based classifiers for speaker dependent spoken digits recognition. The classifier attempted as optimal leading to less number of computations and few components requirement for its future implementation in hardware leading to a low cost speech recognition system. Isolated spoken digits were used as an input data to the neural networks based classifiers. Each spoken word was analyzed for the feature like Mel Frequency Cepstral Coefficients (MFCC). The MLP NN based classifier was designed meticulously with

The wireless links between the nodes together with the dynamicnetwork nature of ad hoc network, increases the challenges of design and implement intrusion detection to detect the attacks. Traditional intrusion detection techniques have had trouble dealing with dynamic environments. In particular, issues such as collects real time attack related audit data and cooperative global detection. Therefore, we are motivated to design a new intrusion detection architecture which involves new detection technique to efficiently detect the abnormalities in the ad hoc networks. In this paper we present the architecture and operation of an intrusion detection technique in Mobile Ad hoc NETwork (MANET). The proposed model has distributed and cooperative architecture. The proposed intrusion detection technique combines the flexibility of anomaly detection with the accuracy of a signature-based detection method. In particular, we exploit machine learning techniques in order to achieve efficient and effective intrusion detection. A series of simulation and experimental results demonstrate that the proposed intrusion detection can effectively detect anomalies with low false positive rate, high detection rate and achieve higher detection accuracy.

The wireless links between the nodes together with the dynamicnetwork nature of ad hoc network, increases the challenges of design and implement intrusion detection to detect the attacks. Traditional intrusion detection techniques have had trouble dealing with dynamic environments. In particular, issues such as collects real time attack related audit data and cooperative global detection. Therefore, we are motivated to design a new intrusion detection architecture which involves new detection technique to efficiently detect the abnormalities in the ad hoc networks. In this paper we present the architecture and operation of an intrusion detection technique in Mobile Ad hoc NETwork (MANET). The proposed model has distributed and cooperative architecture. The proposed intrusion detection technique combines the flexibility of anomaly detection with the accuracy of a signature-based detection method. In particular, we exploit machine learning techniques in order to achieve efficient and effective intrusion detection. A series of simulation and experimental results demonstrate that the proposed intrusion detection can effectively detect anomalies with low false positive rate, high detection rate and achieve higher detection accuracy.

The increased use of digital media to store legal, as well as illegal data, has created the need for specialized tools that can monitor, control and even recover this data. An important task in computer forensics and security is to identify the true file type to which a computer file or computer file fragment belongs. File type identification is traditionally done by means of metadata, such as file extensions and file header and footer signatures. As a result, traditional metadata-based file object type identification techniques work well in cases where the required metadata is available and unaltered. However, traditional approaches are not reliable when the integrity of metadata is not guaranteed or metadata is unavailable. As an alternative, any pattern in the content of a file object can be used to determine the associated file type. This is called content-based file object type identification. Supervised learning techniques can be used to infer a file object type classifier by exploiting some unique pattern that underlies a file type's common file structure. This study builds on existing literature regarding the use of supervised learning techniques for content-based file object type identification, and explores the combined use of multilayer perceptron neural network classifiers and linear programming-based discriminant classifiers as a solution to the multiple class file fragment type identification problem. The purpose of this study was to investigate and compare the use of a single multilayer perceptron neural network classifier, a single linear programming-based discriminant classifier and a combined ensemble of these classifiers in the field of file type identification. The ability of each individual classifier and the ensemble of these classifiers to accurately predict the file type to which a file fragment belongs were tested empirically. The study found that both a multilayer perceptron neural network and a linear programming-based discriminant classifier (used in a round robin) seemed to perform well in solving the multiple class file fragment type identification problem. The results of combining multilayer perceptron neural network classifiers and linear programming-based discriminant classifiers in an ensemble were not better than those of the single optimized classifiers.

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers.

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers.

An analysis of gearbox vibration signals is almost always the default choice when diagnosing the condition of a gearbox because of the rich information contained in the vibration signals and their ease of measurement. Gearbox vibration signals are mostly non-stationary owing to uncertainties associated with the drive and load mechanisms. The non-stationary nature of gearbox vibration signals is evident from an unequal number of samples between successive tachometer pulses. In the present work, independent angular re-sampling (IAR) technique is employed to convert non-stationary vibration signals in the time domain into quasi-stationary signals in the angular domain. The resulting angular domain signals for each gear health condition are merged and then partitioned into a number of data samples which are decomposed using wavelet packet transform. Standard deviation of wavelet packet coefficients is then computed and the distance evaluation technique employed to determine the frequency bands which have a higher distance evaluation factor. Normalized standard deviation values from the optimal frequency bands are then used to train and test an adaptive neurofuzzy inference system (ANFIS). Promising results are obtained when the proposed method is employed to diagnose common faults such as a cracked tooth, chipped tooth and missing tooth in a single stage spur gearbox under fluctuating speed conditions. Gearbox fault diagnosis under fluctuating speed conditions is a more realistic scenario where very limited work has been accomplished.

Stock market Indices prediction is one of the most important issues in the financial field. Although many prediction models have been developed during the last decade, they suffer a poor performance because indices movement is highly non stationary and volati le dynamic process. Artificial Neural Network (ANN) is a technique that is heavily researched and widely used in stock market prediction [1][6]. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. This paper describes various Neural Network models for stock prediction. The prediction was done by, Modular Neural Network, ARIMA-based Neural Networks, Genetic Algorithm, Recurrent Network, Back propagation Network, Radial Basis Function, Branch Network, Functional Link Artificial Neural Network, Feed Forward Neural Network, Fuzzy Neural Network etc [8]. Analysis of all these Neural Network models is performed in this paper, as well as the future work.

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for f...

Globalization has made the stock market prediction (SMP) accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations along with the company's financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN) has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models.

e outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. ese are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. e research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. erefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. is paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. e gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. e analyses help highlight four important gaps in the existing literature on the subject.

In this paper, a Fuzzy based detection system that detects different MANETs attacks is proposed. The proposed system makes use of cluster based architecture to properly organize the nodes in the network. The proposed system use concept of anomaly detection and misuse detection that is based on fuzzy rule sets. The proposed system also makes use of Multilayer Perceptron Neural Network. The Back propagation Neural Network and Feed Forward Neural Network are used to add the results of detection and show the different types of attackers. Advanced Sybil Attack Detection Algorithm is used for the detection of Sybil attack, Wormhole Resistant Hybrid Technique is used for detection of wormhole attack while signal strength and distance is used for detection of hello flood attack. A set of nodes are used for the experimental analysis; 16.54% of the nodes are detected as misbehaving nodes. Hello flood attack is detected at a rate of 98.70%; wormhole attack has a detection rate of 97. 60%; and Sybil attack has a detection rate of 97. 20%.

The vibration signal of a gearbox carries abundant information about its condition and is widely utilized to diagnose the condition of the gearbox. Most of the research efforts in diagnosing gearbox faults, however, assume the acquired vibration signal to be stationary. This paper attempts to highlight the non-stationary nature of gearbox vibration signals owing to uncertainties of the drive and load mechanisms and to synchronize such signals from the revolution point of view. This synchronization is achieved by a simple process referred to as the independent angular re-sampling (IAR) technique. The basis of the IAR technique is the assumption of constant angular acceleration over each independent revolution of the gearbox drive shaft. Through the IAR process, non-stationary signals sampled at constant time increments are converted into quasi-stationary signals sampled at constant angular increments. The angular domain signals obtained from the IAR technique are merged to generate the synchronized angular domain vibration signal for the complete rotational period. The angular domain signal for each gear health condition is partitioned into a number of data samples which are then analyzed using wavelet packet decomposition. Standard deviation values of wavelet packet coefficients are then computed and the resulting vectors utilized as inputs to a multilayer perceptron neural network for identifying the condition of the gearbox.

This paper proposes a novel technique for the condition monitoring of gearboxes based on a self-organizing feature maps (SOF M ) network. In order to visualize the learned SOF M results more clearly, an improved method based on the uni ed distance matrix (U -matrix) method is presented, in which the overall topological information condensed into the map units is considered so as to project the high-dimensional input vectors into a two-dimensional space and give a better picture of their intrinsic structure than the original U -matrix method. The feature data extracted from industrial gearbox vibration signals measured under different operating conditions are analysed using the proposed technique. The results show that trained with the SOF M network and visualized with the improved method, the feature data are mapped into a two-dimensional space and formed clustering regions, each indicative of a speci c gearbox condition. Therefore, the gearbox operating condition with a fatigue crack or broken tooth compared with the normal condition is identi ed clearly. F urthermore, with the trajectory of the image points for the feature data in two-dimensional space, the variation of gearbox conditions is observed visually, and the development of gearbox early-stage failures is monitored in time.

Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.