scholarly journals An Optimization Method for the Geolocation Databases of Internet Hosts Based on Machine Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Ting Wang ◽  
Ke Xu ◽  
Junde Song ◽  
Meina Song
Keyword(s):  
Machine Learning ◽  
Statistical Tests ◽  
Geographic Location ◽  
Network Measurement ◽  
Optimization Method ◽  
Comprehensive Analysis ◽  
The Other ◽  
Routing Policy ◽  
Other Hand ◽  
Improved Performance

In order to improve the accuracy and robustness of geolocation (geographic location) databases, a method based on machine learning called GeoCop (Geolocation Cop) is proposed for optimizing the geolocation databases of Internet hosts. In addition to network measurement, which is always used by the existing geolocation methods, our geolocation model for Internet hosts is also derived by both routing policy and machine learning. After optimization with the GeoCop method, the geolocation databases of Internet hosts are less prone to imperfect measurement and irregular routing. In addition to three frequently used geolocation databases (IP138, QQWry, and IPcn), we obtain two other geolocation databases by implementing two well-known geolocation methods (the constraint-based geolocation method and the topology-based geolocation method) for constructing the optimized objects. Finally, we give a comprehensive analysis on the performance of our method. On one hand, we use typical benchmarks to compare the performance of these databases after optimization; on the other hand, we also perform statistical tests to display the improvement of the GeoCop method. As presented in the comparison tables, the GeoCop method not only achieves improved performance in both accuracy and robustness but also enjoys less measurements and calculation overheads.

scholarly journals Machine Learning in Medical Emergencies: a Systematic Review and Analysis

2021 ◽  
Vol 45 (10) ◽  
Author(s):  
Inés Robles Mendo ◽  
Gonçalo Marques ◽  
Isabel de la Torre Díez ◽  
Miguel López-Coronado ◽  
Francisco Martín-Rodríguez
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Language Processing ◽  
Emergency Services ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
The Other ◽  
Other Hand ◽  
Increasing Demand ◽  
Commercial Applications

AbstractDespite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

Compact Hardware Implementations of MISTY1 Block Cipher

2017 ◽  
Vol 27 (03) ◽  
pp. 1850037 ◽  
Author(s):  
Yasir ◽  
Ning Wu ◽  
Xiaoqiang Zhang
Keyword(s):  
Low Power ◽  
Block Cipher ◽  
Comprehensive Analysis ◽  
The Other ◽  
Key Generation ◽  
Round Function ◽  
Other Hand ◽  
Hardware Implementations ◽  
Covered Area

This paper proposes compact hardware implementations of 64-bit NESSIE proposed MISTY1 block cipher for area constrained and low power ASIC applications. The architectures comprise only one round MISTY1 block cipher algorithm having optimized FO/FI function by re-utilizing S9/S7 substitution functions. A focus is also made on efficient logic implementations of S9 and S7 substitution functions using common sub-expression elimination (CSE) and parallel AND/XOR gates hierarchy. The proposed architecture 1 generates extended key with independent FI function and is suitable for MISTY1 8-rounds implementation. On the other hand, the proposed architecture 2 uses a single FO/FI function for both MISTY1 round function as well as extended key generation and can be employed for MISTY1 [Formula: see text] rounds. To analyze the performance and covered area for ASICs, Synopsys Design Complier, SMIC 0.18[Formula: see text][Formula: see text]m @ 1.8[Formula: see text]V is used. The hardware constituted 3041 and 2331 NAND gates achieving throughput of 171 and 166 Mbps for 8 rounds implementation of architectures 1 and 2, respectively. Comprehensive analysis of proposed designs is covered in this paper.

scholarly journals Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

2020 ◽  
Vol 84 (4) ◽  
pp. 305-314
Author(s):  
Daniel Vietze ◽  
Michael Hein ◽  
Karsten Stahl
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Service Life ◽  
Operating Time ◽  
The Other ◽  
Learning Approaches ◽  
State Of Health ◽  
Remaining Service Life ◽  
Other Hand ◽  
The One

AbstractMost vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

scholarly journals Bachelor Thesis Analytics: Using Machine Learning to Predict Dropout and Identify Performance Factors

2019 ◽  
Vol 1 (1) ◽  
pp. 116 ◽  
Author(s):  
Jalal Nouri ◽  
Ken Larsson ◽  
Mohammed Saqr
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Data Systems ◽  
Academic Institutions ◽  
Other Hand ◽  
Desirable Outcome ◽  
The One

<p class="0abstractCxSpLast">The bachelor thesis is commonly a necessary last step towards the first graduation in higher education and constitutes a central key to both further studies in higher education and employment that requires higher education degrees. Thus, completion of the thesis is a desirable outcome for individual students, academic institutions and society, and non-completion is a significant cost. Unfortunately, many academic institutions around the world experience that many thesis projects are not completed and that students struggle with the thesis process. This paper addresses this issue with the aim to, on the one hand, identify and explain why thesis projects are completed or not, and on the other hand, to predict non-completion and completion of thesis projects using machine learning algorithms. The sample for this study consisted of bachelor students’ thesis projects (n=2436) that have been started between 2010 and 2017. Data were extracted from two different data systems used to record data about thesis projects. From these systems, thesis project data were collected including variables related to both students and supervisors. Traditional statistical analysis (correlation tests, t-tests and factor analysis) was conducted in order to identify factors that influence non-completion and completion of thesis projects and several machine learning algorithms were applied in order to create a model that predicts completion and non-completion. When taking all the analysis mentioned above into account, it can be concluded with confidence that supervisors’ ability and experience play a significant role in determining the success of thesis projects, which, on the one hand, corroborates previous research. On the other hand, this study extends previous research by pointing out additional specific factors, such as the time supervisors take to complete thesis projects and the ratio of previously unfinished thesis projects. It can also be concluded that the academic title of the supervisor, which was one of the variables studied, did not constitute a factor for completing thesis projects. One of the more novel contributions of this study stems from the application of machine learning algorithms that were used in order to – reasonably accurately – predict thesis completion/non-completion. Such predictive models offer the opportunity to support a more optimal matching of students and supervisors.</p>

scholarly journals A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1692 ◽  
Author(s):  
Iván Silva ◽  
José Eugenio Naranjo
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Prediction Models ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
The Other ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbor

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

scholarly journals Iterative Processing for Superposition Mapping

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Tianbin Wo ◽  
Meelis Noemm ◽  
Dapeng Hao ◽  
Peter Adam Hoeher
Keyword(s):  
Coded Modulation ◽  
Comprehensive Analysis ◽  
The Other ◽  
Linear Superposition ◽  
Iterative Processing ◽  
Modulation Technique ◽  
Gaussian Channels ◽  
Other Hand ◽  
Conventional Mapping ◽  
Bit Interleaved Coded Modulation

Superposition mapping (SM) is a modulation technique which loads bit tuples onto data symbols simply via linear superposition. Since the resulting data symbols are often Gaussian-like, SM has a good theoretical potential to approach the capacity of Gaussian channels. On the other hand, the symbol constellation is typically nonbijective and its characteristic is very different from that of conventional mapping schemes like QAM or PSK. As a result, its behavior is also quite different from conventional mapping schemes, particularly when applied in the framework of bit-interleaved coded modulation. In this paper, a comprehensive analysis is provided for SM, with particular focus on aspects related to iterative processing.

scholarly journals A New Statistical Test for PRNG Based on the Attendance’s Law

2021 ◽  
pp. 37-46
Author(s):  
Babacar Alasane Ndaw ◽  
Ousmane Ndiaye ◽  
Mamadou Sanghar´e ◽  
Cheikh Thi´ecoumba Gueye
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Deterministic Algorithm ◽  
Statistical Test ◽  
The Other ◽  
Cryptographic Primitives ◽  
Random Number Generators ◽  
Other Hand ◽  
The One

One family of the cryptographic primitives is random Number Generators (RNG) which have several applications in cryptography such that password generation, nonce generation, Initialisation vector for Stream Cipher, keystream. Recently they are also used to randomise encryption and signature schemes. A pseudo-random number generator (PRNG) or a pseudo-random bit generator (PRBG) is a deterministic algorithm that produces numbers whose distribution is on the one hand indistinguishable from uniform ie. that the probabilities of appearance of the different symbols are equal and that these appearances are all independent. On the other hand, the next output of a PRNG must be unpredictable from all its previous outputs. Indeed, A set of statistical tests for randomness has been proposed in the literature and by NIST to evaluate the security of random(pseudo) bit or block. Unfortunately there are non-random binary streams that pass these standardized tests. In this pap er, as outcome, we intro duce on the one hand a new statistical test in a static contextcalled attendance’s law and on the other hand a distinguisher based on this new attendance’s law.    

scholarly journals Chatbot and its Practical Applications in the Materialistic World

2019 ◽  
Vol 8 (4) ◽  
pp. 4459-4463
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industrial Revolution ◽  
Gradual Increase ◽  
The Other ◽  
Way Of Life ◽  
Practical Applications ◽  
Other Hand ◽  
The World ◽  
Different Types

These days Chat has become the new way of conversation and changed the way of life and the view that the world used to see before and due to Industrial revolution 4.0 , the gradual increase in machine learning and artificial intelligence fields has gone to higher and many companies are reaching customers to get their products with more ease . This is where chatbots are used. It all started with one question! can machines think? The concept of chatbots came into existence to check whether the machines could fool users and make them think that they are actually talking to humans and not robots. On the Other hand, with the Successes Rate of Chat bots, Different companies Started using machines for having conversations with their customers about everything which made their work simpler and reduced the need of man power. There are many different types of building a chatbot but this paper will mainly concentrate on building a Chatbot using TensorFlow API in python

A Review on Deep Learning Applications

2020 ◽  
pp. 21-31
Author(s):  
Chitra A. Dhawale ◽  
Kritika Dhawale ◽  
Rajesh Dubey
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Civil Society ◽  
Use Cases ◽  
The Other ◽  
Strong Ai ◽  
Other Hand

Artificial intelligence (AI) is going through its golden era. Most AI applications are indeed using machine learning, and it currently represents the most promising path to strong AI. On the other hand, deep learning, which is itself a kind of machine learning, is becoming more and more popular and successful at different use cases and is at the peak of developments by enabling more accurate forecasting and better planning for civil society, policymakers, and businesses. As a result, deep learning is becoming a leader in this domain. This chapter presents a brief review of ground-breaking advances in deep learning applications.

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