With the increasing amount of data that are generated everyday worldwide, there can be security p... more With the increasing amount of data that are generated everyday worldwide, there can be security problems with them. So, we have to extend the security needs beyond the traditional approach which emerges the field of cybersecurity. Cybersecurity can handle this big amount of data using deep learning methods. Deep learning is an advanced model of traditional machine learning. It has the aptitude to pull out optimal feature representation from raw input samples. Deep Learning has been successfully applied in various instances in cybersecurity. We can mention here, for example, the intrusion detection, the malware classification, the android malware detection, the spam and phishing detection and the binary analysis. In this paper we describe some of the techniques of Deep learning for cybersecurity.
Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to ... more Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to negative. Machines automatically learn how to detect sentiment without human input, only by training machine learning tools with examples of emotions in text To put it simple, sentiment analysis is the process of analyzing a piece of text and to determine if the writer's attitude towards the subject matter is positive, negative, or neutral. Sentiment analysis models can be trained to read far off trivial definitions, to understand things like, context, sarcasm, and misapplied words. [4] In this paper we have given an application example of sentiment analysis using R, that of the Tolstoy's book, "Anna Karenina". For our analysis, we have used the AFINN, BING and NRC lexicons. Some text analysis, such as bigrams, trigrams etc., is done, as well.
8th International Week Dedicated to Maths 2016, Thessaloniki, Greece, ISBN 978-960-89672-6-7, 2016
In this paper we try to argue through several examples the close relationship which exists betwee... more In this paper we try to argue through several examples the close relationship which exists between mathematics and art.This complex relationship is shown from many mathematicians and artists before many times ago until our days and this paper is a very small contribution on the subject.
International Journal of Engineering Science Invention (IJESI), 2020
The development of algorithmic and problem thinking is very important not only for the school env... more The development of algorithmic and problem thinking is very important not only for the school environment, but also for a large number of activities in real life. In this paper, we try to point out the importance of using the algorithmization in teaching mathematics underlying many of its benefits. Contextually, mathematics and informatics intersect mostly in the term of algorithm, which can be defined as a kind of instructions designed to solve a particular problem..
Quest Journals Journal of Research in Applied Mathematics, 2020
One of the main goals of automated reasoning has been the automation of mathematics. Reasoning is... more One of the main goals of automated reasoning has been the automation of mathematics. Reasoning is the ability to make inferences, and automated reasoning is concerned with the building of computing systems that automate this process. Although the overall goal is to mechanize different forms of reasoning, the term has largely been identified with valid deductive reasoning as practiced in mathematics and formal logic. Building an automated reasoning program means providing an algorithmic description to a formal calculus so that it can be implemented on a computer to prove theorems of the calculus in an efficient manner. [7] Automated reasoning has made a lot of striking successes over the last decades. It evolved into a rich scientific discipline, with many subdisciplines and with solid grounds in mathematics and computer science. Over the years, automated reasoning transformed from a research field based on mathematical logic into a field that is a driving force for mathematical logic. Nowadays, automated reasoning tools are used in everyday practice in mathematics, computer science and engineering. So, automated reasoning programs are being used by researchers to attack open questions in mathematics and logic, provide important applications in computing science, solve problems in engineering, and find novel approaches to questions in exact philosophy. Also, its role in education increases and will increase in the future.
In the 1940's and 50's the computer science made great progress relying on some theoretical devel... more In the 1940's and 50's the computer science made great progress relying on some theoretical developments of the 1930's. The cryptography and machine learning, from the very beginning, were tightly related with this new technology. Cryptography, on the one hand, played an important part during the World War II, where some computers of that time were destined to accomplish cryptanalytic tasks. On the other hand, many authors, such as Turing, Samuel, etc. examined the possibility that computers could "learn" to perform tasks. [1] Machine learning techniques have had a long list of applications in recent years. However, the use of machine learning in information and network security is not new. Machine learning and cryptography have many things in common. The most apparent is the processing of large amounts of data and large search spaces. In its varying techniques, machine learning has been an interesting field of study with massive potential for application. In general, machine learning and cryptanalysis have more in common that machine learning and cryptography. This is due to that they share a common target; searching in large search spaces. A cryptanalyst's target is to find the right key for decryption, while machine learning's target is to find a suitable solution in a large space of possible solutions. [2] In addition to cryptography and cryptanalysis, machine learning has a wide range of applications in relation to information and network security. In these notes, we underline some of them.
ICES-XII "Educational and Social Sciences-Challenges for the Future", Bialystok-Poland, ISBN: 978-9928-214-44-7 , 2020
Roger Bacon (1214-1294), an English Franciscan friar, philosopher, scientist and scholar of the 1... more Roger Bacon (1214-1294), an English Franciscan friar, philosopher, scientist and scholar of the 13th century, has stated: "Neglect of mathematics works injury to all knowledge, since he who is ignorant of it cannot know the other sciences or the things of the world." Generally it is accepted, that mathematics is the most difficult division of geography, but it is becoming a very indispensable discipline. Qualitative analysis alone cannot answer all the questions posed by a modern society with a modern industry and a dynamic activism. A passive knowledge of mathematics is not enough, it must be activated and brought up to date and in accordance with the modern contemporary scientific concepts of the physical world. In this paper we underline some of the numerous applications of mathematics in geography. There are a number of ways in which mathematics is used in geography… Plane Euclidean geometry is used in surveying small areas in the field, while spherical geometry and trigonometry are required in the construction of map projections, both traditional elements of mathematical geography. In the newer applications of mathematics to geography, topology is being used increasingly in the spatial analysis of networks. Graph theory provides indices to describe various types of network, such as drainage patterns. Differential equations are needed to study dynamic processes in geomorphology. Statistical techniques, such as trend surface analysis, factor analysis, cluster analysis and multiple discriminant analysis, can be applied to the description and analysis of the data of regional geography. Mathematical models are used in various forms to simplify the problems in geography. Simulation models and Markov chain stochastic models are of value in studying certain geographical processes… Geography has gained a great deal in quantitative value and precision in adopting mathematical techniques (Cuchlaine A.M. King, 2006).
SEVENTH INTERNATIONAL CONFERENCE ON: “SOCIAL AND NATURAL SCIENCES – GLOBAL CHALLENGE 2020” (ICSNS VII-2020) Vienna-Austria, ISBN: 978-9928-214-04-1, 2020
EPH-International Journal of Mathematics and Statistics, 2019
In this paper we generalize the results of [1] in the case of Ω-monoids. The results we present h... more In this paper we generalize the results of [1] in the case of Ω-monoids. The results we present here are obtained using some concepts of category theory and from a geometric viewpoint. So the proofs are shorter and more simple. The main result is that of Theorem 6.3. which states that if an Ω-monoid í µí± has a finite canonical presentation, then í µí± has FDT
6th International Week Dedicated to Maths 2014, Mathematical meetings looking for the infinity,Thessaloniki,Greece, ISBN: 978-960-89672-5-0, 2014
In this paper we give the main moments of the historical evolution of AI which as we argue is clo... more In this paper we give the main moments of the historical evolution of AI which as we argue is closed related with the development of mathematics in 1870-1930.We illustrate this relationship with some examples from mathematics which are widely used for programming intelligent machines.
Abstract In this paper we generalize the results of C.Squier ([1]) in the case of -monoids. We gi... more Abstract In this paper we generalize the results of C.Squier ([1]) in the case of -monoids. We give, first, the definition of -semigroups and some general results related to the -string rewriting systems, the properties of confluence, termination, Church-Rosser, and so on. Finally, we prove our main theorem which states that if is a finitely presented -monoid which has a presentation involving a finite convergent -string rewriting system , then has finite derivation type.
International Journal of Engineering Inventions, 2023
The idea of quantum computers was developed by Richard Feynman and Yuri Manin. Quantum computatio... more The idea of quantum computers was developed by Richard Feynman and Yuri Manin. Quantum computation is a computational model which is based on the laws of quantum mechanics. Quantum computers can efficiently solve selected problems that are believed to be hard for classical machines. This is achieved by carefully exploiting quantum effects such as interference or likely entanglement. In the situation where the cyberattack are increasing in density and range, Quantum Computing companies, institutions and research groups may become targets of nation state actors, cybercriminals and hacktivists for sabotage, espionage and fiscal motivations. Quantum applications have expanded into commercial, classical information systems and services approaching the necessity to protect their networks, software, hardware and data from digital attacks. Recently, with the introduction of quantum computing, we have observed the introduction of quantum algorithms in Machine Learning. There are several approaches to QML, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM) and Quantum Reinforcement Learning (QRL). In this paper we emphasize the importance and role of QML on cybersecurity.
Machine Learning (ML) is already widely used by companies for many purposes, for example, to incr... more Machine Learning (ML) is already widely used by companies for many purposes, for example, to increase efficiencies or detect problems. ML uses data and algorithms to identify patterns in the data and learn in a similar way with the humans. Quantum computing utilizes algorithms to solve difficult problems much faster than a classical computer. Quantum machine learning (QML) is a new emerging technology that takes advantage of both the quantum computing power and the efficiency of ML. There are several approaches to QML, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM) and Quantum Reinforcement Learning (QRL). In these short notes we will describe some of them highlighting more some of their latest applications.
With the increasing amount of data that are generated everyday worldwide, there can be security p... more With the increasing amount of data that are generated everyday worldwide, there can be security problems with them. So, we have to extend the security needs beyond the traditional approach which emerges the field of cybersecurity. Cybersecurity can handle this big amount of data using deep learning methods. Deep learning is an advanced model of traditional machine learning. It has the aptitude to pull out optimal feature representation from raw input samples. Deep Learning has been successfully applied in various instances in cybersecurity. We can mention here, for example, the intrusion detection, the malware classification, the android malware detection, the spam and phishing detection and the binary analysis. In this paper we describe some of the techniques of Deep learning for cybersecurity.
Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to ... more Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to negative. Machines automatically learn how to detect sentiment without human input, only by training machine learning tools with examples of emotions in text To put it simple, sentiment analysis is the process of analyzing a piece of text and to determine if the writer's attitude towards the subject matter is positive, negative, or neutral. Sentiment analysis models can be trained to read far off trivial definitions, to understand things like, context, sarcasm, and misapplied words. [4] In this paper we have given an application example of sentiment analysis using R, that of the Tolstoy's book, "Anna Karenina". For our analysis, we have used the AFINN, BING and NRC lexicons. Some text analysis, such as bigrams, trigrams etc., is done, as well.
8th International Week Dedicated to Maths 2016, Thessaloniki, Greece, ISBN 978-960-89672-6-7, 2016
In this paper we try to argue through several examples the close relationship which exists betwee... more In this paper we try to argue through several examples the close relationship which exists between mathematics and art.This complex relationship is shown from many mathematicians and artists before many times ago until our days and this paper is a very small contribution on the subject.
International Journal of Engineering Science Invention (IJESI), 2020
The development of algorithmic and problem thinking is very important not only for the school env... more The development of algorithmic and problem thinking is very important not only for the school environment, but also for a large number of activities in real life. In this paper, we try to point out the importance of using the algorithmization in teaching mathematics underlying many of its benefits. Contextually, mathematics and informatics intersect mostly in the term of algorithm, which can be defined as a kind of instructions designed to solve a particular problem..
Quest Journals Journal of Research in Applied Mathematics, 2020
One of the main goals of automated reasoning has been the automation of mathematics. Reasoning is... more One of the main goals of automated reasoning has been the automation of mathematics. Reasoning is the ability to make inferences, and automated reasoning is concerned with the building of computing systems that automate this process. Although the overall goal is to mechanize different forms of reasoning, the term has largely been identified with valid deductive reasoning as practiced in mathematics and formal logic. Building an automated reasoning program means providing an algorithmic description to a formal calculus so that it can be implemented on a computer to prove theorems of the calculus in an efficient manner. [7] Automated reasoning has made a lot of striking successes over the last decades. It evolved into a rich scientific discipline, with many subdisciplines and with solid grounds in mathematics and computer science. Over the years, automated reasoning transformed from a research field based on mathematical logic into a field that is a driving force for mathematical logic. Nowadays, automated reasoning tools are used in everyday practice in mathematics, computer science and engineering. So, automated reasoning programs are being used by researchers to attack open questions in mathematics and logic, provide important applications in computing science, solve problems in engineering, and find novel approaches to questions in exact philosophy. Also, its role in education increases and will increase in the future.
In the 1940's and 50's the computer science made great progress relying on some theoretical devel... more In the 1940's and 50's the computer science made great progress relying on some theoretical developments of the 1930's. The cryptography and machine learning, from the very beginning, were tightly related with this new technology. Cryptography, on the one hand, played an important part during the World War II, where some computers of that time were destined to accomplish cryptanalytic tasks. On the other hand, many authors, such as Turing, Samuel, etc. examined the possibility that computers could "learn" to perform tasks. [1] Machine learning techniques have had a long list of applications in recent years. However, the use of machine learning in information and network security is not new. Machine learning and cryptography have many things in common. The most apparent is the processing of large amounts of data and large search spaces. In its varying techniques, machine learning has been an interesting field of study with massive potential for application. In general, machine learning and cryptanalysis have more in common that machine learning and cryptography. This is due to that they share a common target; searching in large search spaces. A cryptanalyst's target is to find the right key for decryption, while machine learning's target is to find a suitable solution in a large space of possible solutions. [2] In addition to cryptography and cryptanalysis, machine learning has a wide range of applications in relation to information and network security. In these notes, we underline some of them.
ICES-XII "Educational and Social Sciences-Challenges for the Future", Bialystok-Poland, ISBN: 978-9928-214-44-7 , 2020
Roger Bacon (1214-1294), an English Franciscan friar, philosopher, scientist and scholar of the 1... more Roger Bacon (1214-1294), an English Franciscan friar, philosopher, scientist and scholar of the 13th century, has stated: "Neglect of mathematics works injury to all knowledge, since he who is ignorant of it cannot know the other sciences or the things of the world." Generally it is accepted, that mathematics is the most difficult division of geography, but it is becoming a very indispensable discipline. Qualitative analysis alone cannot answer all the questions posed by a modern society with a modern industry and a dynamic activism. A passive knowledge of mathematics is not enough, it must be activated and brought up to date and in accordance with the modern contemporary scientific concepts of the physical world. In this paper we underline some of the numerous applications of mathematics in geography. There are a number of ways in which mathematics is used in geography… Plane Euclidean geometry is used in surveying small areas in the field, while spherical geometry and trigonometry are required in the construction of map projections, both traditional elements of mathematical geography. In the newer applications of mathematics to geography, topology is being used increasingly in the spatial analysis of networks. Graph theory provides indices to describe various types of network, such as drainage patterns. Differential equations are needed to study dynamic processes in geomorphology. Statistical techniques, such as trend surface analysis, factor analysis, cluster analysis and multiple discriminant analysis, can be applied to the description and analysis of the data of regional geography. Mathematical models are used in various forms to simplify the problems in geography. Simulation models and Markov chain stochastic models are of value in studying certain geographical processes… Geography has gained a great deal in quantitative value and precision in adopting mathematical techniques (Cuchlaine A.M. King, 2006).
SEVENTH INTERNATIONAL CONFERENCE ON: “SOCIAL AND NATURAL SCIENCES – GLOBAL CHALLENGE 2020” (ICSNS VII-2020) Vienna-Austria, ISBN: 978-9928-214-04-1, 2020
EPH-International Journal of Mathematics and Statistics, 2019
In this paper we generalize the results of [1] in the case of Ω-monoids. The results we present h... more In this paper we generalize the results of [1] in the case of Ω-monoids. The results we present here are obtained using some concepts of category theory and from a geometric viewpoint. So the proofs are shorter and more simple. The main result is that of Theorem 6.3. which states that if an Ω-monoid í µí± has a finite canonical presentation, then í µí± has FDT
6th International Week Dedicated to Maths 2014, Mathematical meetings looking for the infinity,Thessaloniki,Greece, ISBN: 978-960-89672-5-0, 2014
In this paper we give the main moments of the historical evolution of AI which as we argue is clo... more In this paper we give the main moments of the historical evolution of AI which as we argue is closed related with the development of mathematics in 1870-1930.We illustrate this relationship with some examples from mathematics which are widely used for programming intelligent machines.
Abstract In this paper we generalize the results of C.Squier ([1]) in the case of -monoids. We gi... more Abstract In this paper we generalize the results of C.Squier ([1]) in the case of -monoids. We give, first, the definition of -semigroups and some general results related to the -string rewriting systems, the properties of confluence, termination, Church-Rosser, and so on. Finally, we prove our main theorem which states that if is a finitely presented -monoid which has a presentation involving a finite convergent -string rewriting system , then has finite derivation type.
International Journal of Engineering Inventions, 2023
The idea of quantum computers was developed by Richard Feynman and Yuri Manin. Quantum computatio... more The idea of quantum computers was developed by Richard Feynman and Yuri Manin. Quantum computation is a computational model which is based on the laws of quantum mechanics. Quantum computers can efficiently solve selected problems that are believed to be hard for classical machines. This is achieved by carefully exploiting quantum effects such as interference or likely entanglement. In the situation where the cyberattack are increasing in density and range, Quantum Computing companies, institutions and research groups may become targets of nation state actors, cybercriminals and hacktivists for sabotage, espionage and fiscal motivations. Quantum applications have expanded into commercial, classical information systems and services approaching the necessity to protect their networks, software, hardware and data from digital attacks. Recently, with the introduction of quantum computing, we have observed the introduction of quantum algorithms in Machine Learning. There are several approaches to QML, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM) and Quantum Reinforcement Learning (QRL). In this paper we emphasize the importance and role of QML on cybersecurity.
Machine Learning (ML) is already widely used by companies for many purposes, for example, to incr... more Machine Learning (ML) is already widely used by companies for many purposes, for example, to increase efficiencies or detect problems. ML uses data and algorithms to identify patterns in the data and learn in a similar way with the humans. Quantum computing utilizes algorithms to solve difficult problems much faster than a classical computer. Quantum machine learning (QML) is a new emerging technology that takes advantage of both the quantum computing power and the efficiency of ML. There are several approaches to QML, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM) and Quantum Reinforcement Learning (QRL). In these short notes we will describe some of them highlighting more some of their latest applications.
Fully homomorphic encryption (FHE) has been considered as the "holy grail" of cryptography for it... more Fully homomorphic encryption (FHE) has been considered as the "holy grail" of cryptography for its adaptability as a cryptographic primitive and wide range of potential applications. It opens the door to many new capabilities with the goal to solve the IT world's problems of security and trust. FHE is a new but quickly developing technology. FHE is a cryptographic primitive that allows one to compute arbitrary functions over encrypted data. Since 2009, when Craig Gentry showed that FHE can be realized in principle, there has been a lot of new discoveries and inventions in this particular field and substantial progress has been made in finding more practical and more efficient schemes, as well. Such schemes have numerous applications since it allows users to encrypt their private data locally but still outsource the computation of the encrypted data without risking exposing the actual data. The new schemes significantly reduce the computational cost of FHE and make practical deployment within reach. However, FHE is made possible with many new problems and assumptions that are not yet well studied.
International Journal of Current Science Research and Review, 2022
Machine learning (ML) has grown at a remarkable rate, becoming one of the most popular research d... more Machine learning (ML) has grown at a remarkable rate, becoming one of the most popular research directions. It is widely applied in various fields, such as machine translation, speech recognition, image recognition, recommendation system, etc. Optimization problems lie at the heart of most machine learning approaches. So, the essence of most ML algorithms is to build an optimization model and learn the parameters in the objective function from the given data. A series of effective optimization methods were put forward, in order to promote the development of ML. They have improved the performance and efficiency of ML methods. The aim of this paper is to show that, among many other fields, the grossone may be used successfully in the ML. The grossone, the infinite unit of measure, has been proposed by Professor Y. Sergeyev in a number of noticeable works, as the number of elements of the set, N, of natural numbers. It is expressed by the numeral . This new computational methodology wo...
International Journal of Current Science Research and Review, 2022
Machine learning (ML) has grown at a remarkable rate, becoming one of the most popular research d... more Machine learning (ML) has grown at a remarkable rate, becoming one of the most popular research directions. It is widely applied in various fields, such as machine translation, speech recognition, image recognition, recommendation system, etc. Optimization problems lie at the heart of most machine learning approaches. So, the essence of most ML algorithms is to build an optimization model and learn the parameters in the objective function from the given data. A series of effective optimization methods were put forward, in order to promote the development of ML. They have improved the performance and efficiency of ML methods. The aim of this paper is to show that, among many other fields, the grossone may be used successfully in the ML. The grossone, the infinite unit of measure, has been proposed by Professor Y. Sergeyev in a number of noticeable works, as the number of elements of the set, N, of natural numbers. It is expressed by the numeral ①. This new computational methodology would allow one to work with infinite and infinitesimal quantities in the-same way‖ as that working with finite numbers More details about it are given in Section 4. We analyze the SVM from the viewpoint of mathematical programming, solving a numerical example using the grossone. The Iris dataset was chosen for the implementation of the support vector method. This is a wellknown set of data used in the area of ML.
In this paper we generalize the results of [1] in the case of -monoids. The results we present he... more In this paper we generalize the results of [1] in the case of -monoids. The results we present here are obtained using some concepts of category theory and from a geometric viewpoint. So the proofs are shorter and more simple. The main result is that of Theorem 6.3. which states that if an -monoid has a finite canonical presentation, then has FDT
In this paper we generalize the results of C.Squier ([1]) in the case of -monoids. We give, first... more In this paper we generalize the results of C.Squier ([1]) in the case of -monoids. We give, first, the definition of -semigroups and some general results related to the -string rewriting systems, the properties of confluence, termination, Church-Rosser, and so on. Finally, we prove our main theorem which states that if is a finitely presented -monoid which has a presentation involving a finite convergent -string rewriting system , then has finite derivation type.
MACHINE LEARNING WITH APPLICATIONS, LAP Lambert Academic Publishing, ISBN: 978-620-5-49896-5, 2022
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one a... more The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure. IoT is a promising solution to connect and access every device through internet. Every day the device count increases with large diversity in shape, size, usage and complexity. So IoT drives the world and changes people lives with its wide range of services and applications. Providing numerous services through applications, IoT faces severe security issues, as well. There are existing security measures that can be applied to protect IoT. But, as traditional techniques are not so efficient, a strong-dynamically enhanced and up to date security system is required for next-generation IoT system. A great technological advancement has been noticed in Machine Learning (ML) and Deep Learning (DL). They have opened new possible research windows to address the present and future challenges in IoT. ML&DL are being utilized as a powerful technology for detecting attacks and identifing abnormal behaviors of smart devices and networks.
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