Website Phishing Technique Classification Detection with HSSJAYA Based MLP Training

Website phishing technique is the process of stealing personal information (ID number, social media account information, credit card information etc.) of target users through fake websites that are similar to reality by users who do not have good intentions. There are multiple methods in detecting website phishing technique and one of them is multilayer perceptron (MLP), a type of artificial neural networks. The MLP occurs with at least three layers, the input, at least one hidden layer and the output. Data on the network must be trained by passing over neurons. There are multiple techniques in training the network, one of which is training with metaheuristic algorithms. Metaheuristic algorithms that aim to develop more effective hybrid algorithms by combining the good and successful aspects of more than one algorithm are algorithms inspired by nature. In this study, MLP was trained with Hybrid Salp Swarm Jaya (HSSJAYA) and used to determine whether websites are suspicious, phishing or legal. In order to compare the success of MLP trained with hybrid algorithm, Salp Swarm Algorithm (SSA) and Jaya (JAYA) were compared with MLPs trained with Cuckoo Algorithm (CS), Genetic Algorithm (GA) and Firefly Algorithm (FFA). As a result of the experimental and statistical analysis, it was determined that the MLP trained with HSSJAYA was successful in detecting the website phishing technique according to the results of other algorithms

Advancements in Multi-Layer Perceptron Training to Improve Classification Accuracy

Neural Networks are the popular classification tools used in Medical diagnosis for early disease detection. The performance of Neural Networks is highly depended on the training process. In the training process, the individual weights between each of the neuron are adjusted for better classification results. Many Gradient-based and Meta-heuristic training algorithms are proposed and used by the researchers to improve the training performance of Neural Network. However, there are some limitations in both Gradient-based and Meta-heuristic algorithms when there are used individually. To overcome these limitations and to improve the Multi-Layer Perceptron Network performance Hybrid algorithms are useful. In this study, a review on advancements in Multi-Layer Perceptron Network training process for the improvement of classification performance is presented

Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat

Deep Learning Prediction Models for Runway Configuration Selection and Taxi Times Based on Surface Weather

Growth in air traffic demand in the United States has led to an increase in ground delays at major airports in the nation. Ground delays, including taxi time delays, directly impacts the block time and block fuel for flights which affects the airlines operationally and financially. Additionally, runway configuration selection at an airport significantly impacts the airport capacity, throughput, and delays as it is vital in directing the flow of air traffic in and out of an airport. Runway configuration selection is based on interrelated factors, including weather variables such as wind and visibility, airport facilities such as instrument approach procedures for runways, noise abatement procedures, arrival and departure demand, and coordination of ATC with neighboring airport facilities. The research problem of this study investigated whether runway configuration selection and taxi out times at airports can be predicted with hourly surface weather observations. This study utilized two sequence-to-sequence Deep Learning architectures, LSTM encoderdecoder and Transformer, to predict taxi out times and runway configuration selection for airports in MCO and JFK. An input sequence of 12 hours was used, which included surface weather data and hourly departures and arrivals. The output sequence was set to 6 hours, consisting of taxi out times for the regression models and runway configuration selection for the classification models. For the taxi out times models, the LSTM encoder-decoder model performed better than the Transformer model with the best MSE for output Sequence 2 of 41.26 for MCO and 45.82 for JFK. The SHAP analysis demonstrated that the Departure and Arrival variables had the most significant contribution to the predictions of the model. For the runway configuration prediction tasks, the LSTM encoder-decoder model performed better than the Transformer model for the binary classification task at MCO. The LSTM encoder-decoder and Transformer models demonstrated comparable performance for the multiclass classification task at JFK. Out of the six output sequences, Sequence 3 demonstrated the best performance with an accuracy of 80.24 and precision of 0.70 for MCO and an accuracy of 77.26 and precision of 0.76 for JFK. The SHAP analysis demonstrated that the Departure, Dew Point, and Wind Direction variables had the most significant contribution to the predictions of the model

Through space and time

Modern fishing vessels have a wide range of instruments and sensors on board that are used for active fishing operations, with sonar equipment and echo sounders being among the most common. Sonar allows horizontal observation of the water column, while echo sounders provide more precise underwater environment monitoring. These instruments are useful as they are used today but require a lot of user experience for effective use. Estimating biomass den- sity, fish size, and species is highly demanding, and the existing systems have significant uncertainties. In this thesis, we propose a novel approach to hydroacoustic data analysis that capitalizes on catch reports as annotations for hydroacoustic transects. Com- bining catch messages with the positional attribute of echo data allows us to obtain annotated echo examples that describe the biota within a given loca- tion. The thesis leverages EchoBERT, a BERT-inspired model, as the underlying architecture. To assess the capabilities of the annotations, we evaluate the model using dif- ferent types of models. Both classification and regression tasks are employed, wherein the classification task aims to predict the presence of a species based on catch messages. In contrast, the regression tasks attempt to fit the model to the catch data and generate a distribution of the species. Furthermore, we assess the model considering timestamps. Since the catch messages may not necessarily correspond to the same date as the echo data, we incorporate weighted loss functions that account for the temporal proximity. This approach allows for a closer association during the training process, where the outcome is weighted more heavily for temporally closer labels. Our results provide insight into the characteristics of catch reports as anno- tations, shedding light on their usefulness and limitations. We also uncover potential bias present in the labelled data, where a seasonal fishing activity can be uncovered in the dataset. We also experiment and find the magnitude of difference in collation criterion when finding catch data based on the haversines formula