Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classification framework is ideal, as it can be customized for use in a wide variety of networks. In this paper, we propose a highly configurable and flexible machine learning traffic classification method that relies only on statistics of sequences of packets to distinguish known, or approved, traffic from unknown traffic. Our method is based on likelihood estimation, provides a measure of certainty for classification decisions, and can classify traffic at adjustable certainty levels. Our classification method can also be applied in different classification scenarios, each prioritizing a different classification goal. We demonstrate how our classification scheme and all its configurations perform well on real-world traffic from a high performance computing network environment.

This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A). Machine learning appears well suited to support FP\&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP\&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenised it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: i) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); ii) A framework to decrease the formal context's ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 samples corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standad one using concept latticeinvariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios. Keywords Concept hierarchies, formal context reduction, concept lattice reduction, adaptive ECA*, FCA, WordNet. 1. Introduction The Semantic Web is an extended web of machine-readable data, which provides a program to process data via machine directly or indirectly [1]. As an expansion of the latest Web, the Semantic Web can add meaning to the World Wide Web content and thus support automated services on the basis os semantic representations. Meanwhile, the Semantic Web depends on structured ontologies to organize the underlying data and provide a detailed and portable interpretation of computing machines [2].

Let's take an example suppose you see two people Ashutosh & Abhishek and guessed that Abhishek is obese and Ashutosh is non-obese. But If you want to perform same task using machine how would you perform. Here Classification based models come into picture. Based on the data that has been provided to machine, it will predict whether the person is obese or not. Logistic Regression is the combination of Linear Regression and Sigmoid Function.

Artificial Intelligence Technologies and Sustainability of Our Environment Taniya Basu Wed, 07/07/2021 – 21:01 Log in or register to post comments Introduction: In recent years, the environmental issues have triggered debates, discussions, awareness programs and public outrage that have catapulted interest in new technologies, such as Artificial Intelligence. Artificial Intelligence finds application in environmental sectors, including natural resource conservation, wildlife protection, energy management, clean energy, waste management, pollution control and agriculture. Advancement in the AI in environmental protection market could be one of the solutions to solve the major environmental concerns. The application of AI in environment protection includes machine learning for protecting the oceans, monitoring shipping, ocean mining, fishing, coral bleaching or the outbreak of marine disease. The AI techniques are quite beneficial for environmental analysis, as they are able to process a huge amount of data quickly so as to draw conclusions that may have not been possible by humans. The AI techniques are quite beneficial for environmental analysis, as they are able to process a huge amount of data quickly so as to draw conclusions that may have not been possible by humans.  1.Weather Forecasting & Climate Changes: The traditional models of weather forecasting are based on statistical measures of numeric models, and it does not give answers in binary. The data collected can be from deep space satellites, weather balloons, radar systems, nowcasting weather warnings and environmental analytics and sometimes from IoT based sensors.  The AI predictions are primarily based on machine learning algorithms. By processing more complex data in a shorter span of time using linear regression principles, now meteorologists can make predictions with improved accuracy and thus saves lives and money. Machine learning can abet with other forecasts as well, including temperature, wave height, and precipitation. Google’s AI forecast tool that is based on the UNET convolutional neural network (CNN) allows researchers to generate accurate rainfall predictions six hours ahead of when the precipitation occurs. CNN is a sequence of layers of mathematical operations arranged in an encoding phase. It takes the input satellite imagery and then transforms them into output images. 2.Climate Changes: For instance, we can halt emissions in the energy sector by using AI technology to forecast the supply and demand of power in the grid, improve the scheduling renewables, and reduce the life-cycle fossil fuel emissions through predictive maintenance. AI applications in transportation can enable more accurate traffic predictions, the development of freight transportation, and better modelling of demand and shared mobility option. Other kinds of impacts include the waste that is disrupting ecosystems, pollutants that affect human and animal health and biodiversity loss. By harnessing the swaths of data from sensors and satellites, we can better predict climate change impacts and proactively steward these ecosystems.  AI applied in food systems can help better monitor crop yields, reduce the need for chemicals and excess water through precision agriculture and minimize food waste through forecasting demand and identifying spoiled produce. Lastly, AI systems used in buildings and cities can help automatically control heating and cooling as well as model energy used to decide which buildings to retrofit.  3.Biodiversity and Conservation: With the recent development of AI-powered devices for the conservation of animals, we can now prevent wildlife extinction. After the extinction of western African rhinoceros, African elephants are next on the verge of going extinct due to the involvement of extensive poaching. The AI-based technology system uses a camera that detects poachers planning to attack an animal and subsequently generates an alert to the park rangers in real Plants are very beneficial for human lives and greatly help in fulfilling our necessities. They help fulfill our basic necessities as they can provide us with food, shelter, and medicine. The more the number of trees present in an environment, the greater is the amount of oxygen produced. The AI-based platform allows its users to click and share photos of various species of plants in real time. It also allows the other community members to identify the photos of the specific plant and confirm the plant’s presence, whether if such a plant already exists. In this way, the AI-based networking platform can help discover new species of plants worldwide. 4.Ocean Health: In a recent research by two AI algorithm— Latent Variable Gaussian Process (LVGP) model and Probabilistic Principal Component Analysis (PPCA) were used to understand the sonar echoes in the ocean. The research aimed at observing the changes that can happen with sonar echoes at different depths, salinity, and temperature. The algorithms were capable of classifying underwater environments from simulated sonar measurements with an average accuracy of more than 90%.  The application of artificial intelligence, ML algorithms, and smart robots seems to be the perfect combination in the future to come. Deep-sea mining and deep-sea research without disturbing the life beneath seem difficult a few years before, but not anymore. With the application of these latest technologies, oceanographers can create accurate cartography, understand the impact of climate change, species status, salinity, and gather a large amount of data to explore the areas left behind. Conclusion: Researchers and scientists must ensure that the data provided through Artificial Intelligence systems are transparent, fair and trustworthy. With an increasing demand of automation solutions and higher precision data-study for environment related problems and challenges, more multinational companies, educational institutions and government sectors need to fund more R&D of such technologies and provide proper standardizations for producing and applying them. In addition, there is a necessity to bring in more technologists and developers to this technology. Artificial intelligence is steadily becoming a part in our daily lives, and its impact can be seen through the advancements made in the field of environmental sciences and environmental management. Attachment AI IN ENVIRONMENT TECH.pdf Cover Image Image Publish Location Tech for Good

A research team at the University of Córdoba has developed and evaluated models for the prediction of solar radiation in nine locations in southern Spain and North Carolina (USA). Measuring solar radiation is costly, as are all the tasks related to the maintenance and calibration of the most commonly used sensors: pyranometers and radiometers. The result is a paucity of reliable data. Hence, a research group from the University of Córdoba has developed and evaluated several Machine Learning models to predict solar radiation in nine locations (southern Spain and North Carolina, USA) spanning a range of different geo-climatic conditions (aridity, distance to the sea, and elevation). The work has been featured in the journal Applied Energy.

A research team at the University of Córdoba has developed and evaluated models for the prediction of solar radiation in nine locations in southern Spain and North Carolina (USA). Measuring solar radiation is costly, as are all the tasks related to the maintenance and calibration of the most commonly used sensors: pyranometers and radiometers. The result is a paucity of reliable data. Hence, a research group from the University of Córdoba has developed and evaluated several Machine Learning models to predict solar radiation in nine locations (southern Spain and North Carolina, USA) spanning a range of different geo-climatic conditions (aridity, distance to the sea, and elevation). The work has been featured in the journal Applied Energy.

Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from the raw data. What makes it different from statistics is that data scientists use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. A Data Scientist will look at the data from many angles, sometimes angles not known earlier. Data Visualization is one of the most important branches of data science. It is one of the main tools used to analyze and study relationships between different variables.

Gradient descent (GD) type optimization schemes are the standard methods to train artificial neural networks (ANNs) with rectified linear unit (ReLU) activation. Such schemes can be considered as discretizations of gradient flows (GFs) associated to the training of ANNs with ReLU activation and most of the key difficulties in the mathematical convergence analysis of GD type optimization schemes in the training of ANNs with ReLU activation seem to be already present in the dynamics of the corresponding GF differential equations. It is the key subject of this work to analyze such GF differential equations in the training of ANNs with ReLU activation and three layers (one input layer, one hidden layer, and one output layer). In particular, in this article we prove in the case where the target function is possibly multi-dimensional and continuous and in the case where the probability distribution of the input data is absolutely continuous with respect to the Lebesgue measure that the risk of every bounded GF trajectory converges to the risk of a critical point. In addition, in this article we show in the case of a 1-dimensional affine linear target function and in the case where the probability distribution of the input data coincides with the standard uniform distribution that the risk of every bounded GF trajectory converges to zero if the initial risk is sufficiently small. Finally, in the special situation where there is only one neuron on the hidden layer (1-dimensional hidden layer) we strengthen the above named result for affine linear target functions by proving that that the risk of every (not necessarily bounded) GF trajectory converges to zero if the initial risk is sufficiently small.

In this paper, we propose Attention Based Decomposition Network (ABD-Net), for point cloud decomposition into basic geometric shapes namely, plane, sphere, cone and cylinder. We show improved performance of 3D object classification using attention features based on primitive shapes in point clouds. Point clouds, being the simple and compact representation of 3D objects have gained increasing popularity. They demand robust methods for feature extraction due to unorderness in point sets. In ABD-Net the proposed Local Proximity Encapsulator captures the local geometric variations along with spatial encoding around each point from the input point sets. The encapsulated local features are further passed to proposed Attention Feature Encoder to learn basic shapes in point cloud. Attention Feature Encoder models geometric relationship between the neighborhoods of all the points resulting in capturing global point cloud information. We demonstrate the results of our proposed ABD-Net on ANSI mechanical component and ModelNet40 datasets. We also demonstrate the effectiveness of ABD-Net over the acquired attention features by improving the performance of 3D object classification on ModelNet40 benchmark dataset and compare them with state-of-the-art techniques.