Logotipo do Freelancer Como Funciona Busque Trabalhos Fazer Login Cadastrar Publicar um ProjetoProfile cover photo
Agora você está seguindo .
Erro seguindo usuário.
Esse usuários não permite que o sigam.
Você já está seguindo esse usuário.
Seu plano permite apenas 0 seguidas. Aprimore-o aqui.
Deixou de seguir o usuário com sucesso.
Ocorreu um erro ao deixar de seguir o usuário.
Você recomendou com sucesso
Ocorreu um erro ao recomendar o usuário.
Algo deu errado. Por favor, atualize a página e tente novamente.
E-mail verificado com sucesso
Avatar do Usuário
$50 USD / hora
Bandeira do(a) UZBEKISTAN
tashkent, uzbekistan
$50 USD / hora
No momento são 3:07 AM aqui
Entrou no Freelancer em novembro 27, 2021
1 Recomendação

Samidullo A.

@Samidullo

5,0 (11 avaliações)
4,0
4,0
$50 USD / hora
Bandeira do(a) UZBEKISTAN
tashkent, uzbekistan
$50 USD / hora
100%
Trabalhos Concluídos
89%
Dentro do Orçamento
93%
No Prazo
5%
Taxa de Recontratação

Data scientist/Django Expert/Cryptography Expert

Welcome to my Freelancer Profile! I am a Machine learning Engineer whose focus is in Multi / Logistic/Linear Regression. I enjoy developing new and novel models as well as creating infrastructure for automation and ease-of-use. Data of all types interest me. I have experience developing Machine learning applications for a variety of hardware architectures, including mobile devices.. Working with me you will get best practice in source code management, version control, and security access to your code base. My technology stack is listed in long-form below. Core: - Linux/Ubuntu - Python 3 Data Manipulation: - Numpy - Pandas - OpenCV - PIL/Pillow Machine Learning - Scikit-learn -SciPy Deep Learning: - Tensorflow - Keras - PyTorch Data mining : -Weka Data Visualization: -Tableau - Matplotlib - Seaborn Thanks for your consideration!
Freelancer Python Developers Uzbekistan

Contate Samidullo A. para falar sobre o seu trabalho

Entre para falar sobre qualquer detalhe pelo chat

Itens de Portfólio

A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers.

In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
A5/1 is the strong version of the encryption algorithm used by about 130 million GSM customers in Europe to protect the over-the-air privacy of their cellular voice and data communication. The best published attacks against it require between 240 and 245 steps. This level of security makes it vulnerable to hardware-based attacks by large organizations, but not to software-based attacks on multiple targets by hackers.

In this paper we describe new attacks on A5/1, which are based on subtle flaws in the tap structure of the registers, their noninvertible clocking mechanism, and their frequent resets. After a 248 parallelizable data preparation stage (which has to be carried out only once), the actual attacks can be carried out in real time on a single PC.
A5/1 Cryptography algorithm
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline.
DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
Data encryption standard (DES) has been found vulnerable against very powerful attacks and therefore, the popularity of DES has been found slightly on the decline.
DES is a block cipher and encrypts data in blocks of size of 64 bits each, which means 64 bits of plain text goes as the input to DES, which produces 64 bits of ciphertext. The same algorithm and key are used for encryption and decryption, with minor differences. The key length is 56 bits. The basic idea is shown in the figure.
Data encryption standard (DES)
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
A US shop is trying to forecast future traffic and sales based on historical data gathered over the years. The initial data provided is split into two different datasets, the traffic one contains data from roughly 2015 to 2018 while the sales csv file presents more data points starting from 2013 and also finishing in 2018.
Successfully predicting future income and affluence can be extremely important for companies in order to come up with effective strategies encompassing internal logistics and marketing initiatives.
Given the nature of the two datasets, it seemed like time series analysis was the most appropriate choice for this type of problem.
An external dataset containing federal US holidays was also merged with the two initial ones in order to provide some additional insights. The external dataset can be found on Kaggle. It simply states all the federal holidays from 1966 to 2020. This dataset was combined with the other two only for EDA purposes but not for modelling ones.
Traffic and Sales Analysis for US shop
Task is to write a program in any programming language supported on our Linux CSE machines
that will decrypt as much of the message as possible using the fact that Alice and Bob reused their
one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file
appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying
some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with
60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits).
The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in
bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic
characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines
that will decrypt as much of the message as possible using the fact that Alice and Bob reused their
one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file
appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying
some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with
60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits).
The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in
bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic
characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Task is to write a program in any programming language supported on our Linux CSE machines
that will decrypt as much of the message as possible using the fact that Alice and Bob reused their
one-time pad for all of the six messages that Eve stored. This will simply involve reading in the file
appropriately (i.e., using two hexadecimal values for each encrypted character) and then applying
some techniques to decrypt the ciphertexts. You may assume that you have six ciphertexts, each with
60 characters expressed using their hexadecimal ASCII values (i.e., 2 hexadecimal values or 8 bits).
The one-time pad used to create each ciphertext is exactly the length of the plaintext message (in
bytes, that is, 120 bytes). Also, the original plaintexts contain only upper- and lowercase alphabetic
characters with spaces (i.e., no special characters or punctuation).
Decryption One-Time-Pad encrypted code without Passwork
Working with ML models : J48 Tree, Naive Bayes Simple, RBF Network, IB1
Using Weka knowledge flow
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data
K-Means is implicitly based on pairwise Euclidean distances between data points, because the sum of squared deviations from centroid is equal to the sum of pairwise squared Euclidean distances divided by the number of points. The term "centroid" is itself from Euclidean geometry.
Unsupervised Kmeans algorithm used to clusted Iris data

Avaliações

Mudanças salvas
Mostrando 1 - 5 de 11 avaliações
Filtrar avaliações por: 5,0
£80,00 GBP
Very impressed with the quality of work delivered! would highly recommend this freelancer for any data analytics project.
Data Processing Excel Data Mining Data Analytics Data Cleansing
A
Bandeira do(a) Jack A. @Azeez10
há 8 meses
5,0
₹2.000,00 INR
Good job done on the project
Python Software Architecture Statistics Machine Learning (ML)
H
Bandeira do(a) Harsh K. @harshal37
há 9 meses
5,0
₹1.500,00 INR
It is very easy to work with you. You are very polite and professional.
Matlab and Mathematica Mathematics Linear Programming MATLAB
+1 mais
V
Bandeira do(a) Simon A. @VIPCorp
há 10 meses
5,0
$75,00 USD
Sami deliver work on time, doing work professionally, happy to work with this freelancer!!
Django Data Visualization Data Architecture Computer Vision
+1 mais
Avatar do Usuário
Bandeira do(a) Waqas A. @vicky66
há 10 meses
5,0
₹23.150,00 INR
Good Work. Thanks! Have a nice day.
Python Software Architecture
V
Bandeira do(a) Simon A. @VIPCorp
há 1 ano

Qualificações

AI engineering

IBM
2021
About this Course This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

Contate Samidullo A. para falar sobre o seu trabalho

Entre para falar sobre qualquer detalhe pelo chat

Verificações

Freelancer Preferencial
Identidade Verificada
Pagamento Verificado
Telefone Verificado
E-mail Verificado
Conectado ao Facebook

Certificações

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 92%

Principais Habilidades

Python 6 Data Mining 5 Machine Learning (ML) 4 Data Science 4 Algorithm 3

Busque Freelancers Parecidos

Python Developers in Uzbekistan
Python Developers
Data Mining Experts
Machine Learning Experts

Busque Mostruários Parecidos

Python
Data Mining
Machine Learning (ML)
Data Science
Usuário Anterior
Usuário Seguinte
Convite enviado com sucesso!
Usuários Registrados Total de Trabalhos Publicados
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2023 Freelancer Technology Pty Limited (ACN 142 189 759)
Carregando pré-visualização
Permissão concedida para Geolocalização.
Sua sessão expirou e você foi desconectado. Por favor, faça login novamente.