Information security has progressed dramatically over the past century because of the significant advancements in computer and communications research. As a result, numerous scholars have made it their major goal to examine, improve, implement, or compare its methods. Cryptography is one of the most critical information security topics, with many different encryption algorithms, such as symmetric key and public key encryption methods, branching out from it. Homomorphic encryption is a form of encryption that permits mathematical operations on a ciphertext; it has recently become famous for protecting data in the cloud. This paper offers essential knowledge to researchers who may want it before implementing homomorphic/public key methods. It analyses the unpadded RSA, Paillier, and ElGamal public-key algorithms. The evaluation is done according to several criteria, including secrecy, homomorphism property, encryption/decryption time, memory usage, encryption throughput criteria, and how different file sizes and key sizes affect these criteria. The results demonstrate that the three algorithms compete in the analysed criteria. Depending on the application and the required features, any one of the algorithms might be favoured over the other. 

Performance Evaluation of RSA, ElGamal, and Paillier Partial Homomorphic Encryption Algorithms

There is growing on-going research into how footballer attributes, collected prior to, during and post-match, may address the demands of clubs, media pundits and gaming developers. Focusing upon individual player performance analysis and prediction, we examined the body of research which considers different player attributes. This resulted in the selection of 132 relevant papers published between 1999 and 2020. From these we have compiled a comprehensive list of player attributes, categorising them as static, such as age and height, or dynamic, such as pass completions and shots on target. To indicate their accuracy, we classified each attribute as objectively or subjectively derived, and finally by their implied accessibility and their likely personal and club sensitivity. We assigned these attributes to 25 logical groups such as passing, tackling and player demographics. We analysed the relative research focus on each group and noted the analytical methods deployed, identifying which statistical or machine learning techniques were used. We reviewed and considered the use of character trait attributes in the selected papers and discuss more formal approaches to their use. Based upon this we have made recommendations on how this work may be developed to support elite clubs in the consideration of transfer targets.

The collection, analysis and exploitation of footballer attributes: A systematic review

We designed a multilayer perceptron neural network to predict the price of a football (soccer) player using data on more than 15,000 players from the football simulation video game FIFA 2017. The network was optimized by experimenting with different activation functions, number of neurons and layers, learning rate and its decay, Nesterov momentum based stochastic gradient descent, L2 regularization, and early stopping. Simultaneous exploration of various aspects of neural network training is performed and their trade-offs are investigated. Our final model achieves a top-5 accuracy of 87.2% among 119 pricing categories, and places any footballer within 6.32% of his actual price on average.

Pricing Football Players using Neural Networks

Today, in the computerized age, the development and use of internet technology in healthcare has been increasing rapidly. Quick improvement is going on in the telemedicine field with a clear goal to design integrated applications in diagnostics and treatment. Hence medical images are preserved in databases for some time so that post diagnosis treatment will become easier for the patient and for doctors as well. During the process of the treatment patients tend to take experts' advice and that time images of the reports will be shared from one hospital to another. With the use of sharing and transmission technique post treatment cost gets reduced whereas increases the issue concerning the security of patients medical reports. It is important to keep track of images shared between two medical centers to identify sources of image generation as well as protecting patient's sensitive information from intruders or hackers against unauthorized access and modification. Digital watermarking can be used to provide ownership and copyright protection. In the watermarking system image or text can be inserted in the cover image to get a watermarked image. The watermark image or secret image to be inserted can be protected with encryption technique. Hence this paper presents a digital watermarking system designed with encryption techniques to provide security to patients' medical records. The system is evaluated by implementing it with various encryption techniques like Advanced Encryption Standard (AES) with 256 bits, AES-GCM, Triple Data Encryption Standard (TDES), Blowfish etc. The performance of the system is measured with PSNR and SSIM and assessment of the evaluation parameters shows that AES encryption is better in comparison with TDES and Blowfish. 

Analysis of Different Encryption Techniques used in Watermarking Algorithm for the Security of Medical Image

Introduction: European team presumes to have a great development project to feed their first team and to create elite players. Objective: Building on a dataset of homegrown player transfers, national football league’s revenues, and European football revenues from the 2015/2016 season to the 2020/2021 season inside the five major leagues (LaLiga, Premier League, Ligue 1, Bundesliga, and Serie A). Materials and methods: Were exanimated 74 football teams in to know how profitable their projects were. Results and discussion: Results show that does not exist only one way to take advantage of developing homegrown football players. Conclusions: These results vary according to each football club’s objectives, which could be different with the passing of the years.

Economic contribution and sport success through the sale of homegrown football players from the five European major leagues

The global transfer market in the field of football has long been devoid of technology. Over the last two decades, the economic dynamics have completely been changed, and the amount of money which has been pumped has increased exponentially. Valuations for any physical entity with a well-defined set of parameters is a challenge in itself and depends on numerous other things. And doing the same for an individual with varying parameters, based on the source of data, is even more challenging. With this paper, we attempt to find the most optimum way of fetching data of football players and applying the right model on it so as to extract meaningful information, thus reducing the gap between the estimated prices and the Final price. We do so by crowdsourcing the data and applying regression techniques along with Opta index. We also try to find the results using Neural Networks and conclude with a comparison between our models

A study of Prediction models for football player valuations by quantifying statistical and economic attributes for the global transfer market

In today’s world, data is termed as the new gold. The need for providing security to the data stored on local storage or at a remote location is a must. Various methods and algorithms are used for securing data one of which is using symmetric key cryptography. It is an encryption technique which uses a single key for the encryption and decryption process. This paper provides an extensive literature survey and puts forth a comparative analysis of the existing symmetric cryptography algorithms (Caesar, Vigenere, Data Encryption Standard, Triple Data Encryption Standard, Extended Data Encryption Standard, Rijndael, Twofish, Blowfish, Modified Blowfish) based on their architecture, flexibility, reliability, security and also states the limitations for each that are essential for secure communication through a wired or wireless medium.

Krishna Samdani

Papers

A study of Prediction models for football player valuations by quantifying statistical and economic attributes for the global transfer market

A Comprehensive Analysis Between Popular Symmetric Encryption Algorithms