Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for \\Weakly Labeled Semi-supervised Sound Event Detection

In this paper we present our system for thedetection and classi-fication of acoustic scenes and events (DCASE) 2020 ChallengeTask 4: Sound event detection and separation in domestic envi-ronments. We introduce two new models: the forward-backwardconvolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional neural network (CNN). The FBCRNNemploys two recurrent neural network (RNN) classifiers sharing thesame CNN for preprocessing. With one RNN processing a record-ing in forward direction and the other in backward direction, thetwo networks are trained to jointly predict audio tags, i.e., weak la-bels, at each time step within a recording, given that at each timestep they have jointly processed the whole recording. The pro-posed training encourages the classifiers to tag events as soon aspossible. Therefore, after training, the networks can be appliedto shorter audio segments of, e.g.,200 ms, allowing sound eventdetection (SED). Further, we propose a tag-conditioned CNN tocomplement SED. It is trained to predict strong labels while using(predicted) tags, i.e., weak labels, as additional input. For train-ing pseudo strong labels from a FBCRNN ensemble are used. Thepresented system scored the fourth and third place in the systemsand teams rankings, respectively. Subsequent improvements allowour system to even outperform the challenge baseline and winnersystems in average by, respectively,18.0 %and2.2 %event-basedF1-score on the validation set. Source code is publicly available athttps://github.com/fgnt/pb_sed

Analysis of Wheat Samples Using the Calculation of Multifractal Spectrum

The computational analysis of wheat images to identify wheat varieties and quality has wide applications in agriculture and production. This paper presents an approach to the analysis and classification of images of wheat samples obtained by the method of crystallization with additives. In tests 3 concentration and 4 times for each concentration were used, such that each type of wheat was characterized by 12 images. We used the images obtained for 5 classes. All the images have similar visual characteristics, that makes it difficult to use statistical methods of analysis. The multifractal spectrum obtained by calculating the local density function was used as a classifying feature. The classification was performed on a set of 60 wheat images corresponding to 5 different samples (classes) by various machine learning methods such as linear regression, naive Bayesian classifier, support vector machine, and random forest. In some cases, to reduce the dimension of the feature space the method of principal components was applied. To identify the relationships between wheat samples obtained at different concentrations, 3 different clustering methods were used. The classification results showed that the multifractal spectrum as classifying sign and using the random forest method in combination with the principal component analysis allow identifying wheat samples obtained by crystallization with additives, being the highest average classi- fication accuracy is 74 %.

GULFA MARMA IN RELATION TO THE MODERN HUMAN ANATOMY

Marma is originated from the Sanskrit root word etymologically, ‘Mr’- Marne and the term ‘Sthana’ signi-fies the location. This jointly signifies the vitality of Marma in the human body. Any kind of injury to these parts of body may cause sensory or functional deformity or severe haemorrhage or even collapse and death instantaneously or lately. The Marma have been included as one of the important chapters in Sharir Sthana of Sushruta Samhita. In which Marma are categorized according to fatality, dimensions, integrity etc. Marma has a common factor as being a seat of Prana or seat of life in Ayurveda literature. Marma therapy focuses on stimulation of these points for activation of Prana factor in management of related dis-orders. But only described as danger spots of body Marma points have gained a lot of therapeutic im-portance nowadays. Considering present modern anatomy Marma being a physical entity also should be explored parallelly as it still lacks the adequate and comprehensive western science description. The meas-urements are given in Anguli Pramana of the person himself. Sushruta has described the anatomical classi-fication of Marma which makes it a little easier to explore them. This will lead to a proper understanding, for better learning and practice of Marma. Gulfa Marma is explored anatomically and in similarity to struc-ture and various other characteristics with modern anatomy.

ANALYSIS OF CREATING BENCHMARK IMAGES METHODS TO TEST THE ACCURACY OF PHOTOGRAMMETRIC SOFTWARE

Digital photogrammetry is based on the use of specialized photogrammetric software (or digital photogrammetric systems) to solve problems related to the aerospace imagery processing. A wide range of programs and high price motivate consumers to choose the right software that responds to requirements of processing accuracy, amount of work, time of execution, etc. The main goal of this study is to analyze the existing methods of benchmark images creating to test photogrammetric pro-grams. The article carries out the analysis of existing techniques of creating benchmark images, classi-fication, selection of benchmark images types suitable for testing of photogrammetric software, and substantiates the necessity for checking of aerial survey results quality in specialized software.

CONSTRUCTION OF A DATA CLUSTERING MODEL EXEMPLIFIED BY DEMO-GRAPHIC INDICATORS OF THE FEFD REGIONS

The article deals with the problem of constructing a model for classifying the regions of the Far Eastern Federal District on the basis of demographic data with the use of machine learning algo-rithms - t-distributed Stochastic Neighbor Embedding, K-means and self-organizing networks. Column diagrams and heat maps of correlation coefficients are built for demographic indicators. It is proposed to replace demographic indicators with rank values. The effect it has on the classi-fication results is studied. The classifier has been built on the basis of a self-organizing network, that allows the regions of the Far Eastern Federal District to be classified as belonging to one of the classes: depressed, satisfactory or good.

Identification and Classification of Color Textures

This article describes, how color textures can be reliably detected and classified in the production process independent of external parameters such as brightness, object positions (translation), angulars (rotation), object distances (scaling) or curved surfaces (rotation + scaling). The methods described here are also suitable for reliably classifying at least 18 color textures even if they differ only slightly from each other optically. The online classification of color textures is a classic task in the wood, furniture and textile industry. For example, un- wanted defects or partial soiling on moving webs can be reliably detected regard- less of fluctuations in brightness and/or shadows during process operation. Algo- rithms has been developed for teach-in with RGB-HSI-transform, set fewer seg- ments on the color textures of each class with e.g. 24x24 Pixel, use suitable transformations {HSI}, e.g. 2D-FFT for formation characteristic 2D spectral mountains in these segments, extraction of statistical features and setting up the individual classifiers. Algorithms has been developed for identification & classification in process op- eration with extraction of statistical characteristics and methods of robust classi- fication. The implementation of the methods, the triggering of the color cameras, the processing of the color information including the output of the results to the process control is done with the data analysis program Xeidana®.

New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.

New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.