Browsing by Author "Akeem Femi Kadri"

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    An Enhanced Message Encryption Approach Using Residue Number System
    (Kasu Journal of Computer Science, 2024) Akeem Femi Kadri; Kafayat Odunayo Tajudeen; Ayisat Wuraola Asaju-Gbolagade
    Safe transmission of messages over the internet is ensured by data security. The data should be protected against unauthorized access and transmitted to the legal recipient safely and formally called cryptography. The well-known cryptography types are asymmetric and symmetric and different symmetric encryption algorithms such as Advanced Encryption Standard (AES), Data Encryption Standard (DES), Blowfish were proposed to protect the confidentiality of the transmitted and stored data. However, most of these symmetric techniques are vulnerable to brute-force attack because the cipher text remains unintelligible for the original data to be found. Consequently, this challenge prompted researchers to introduce Residue Number System for encryption. RNS helps to withstand the vulnerability of the encryption algorithms in brute-force attacks making transmitted and stored data more secure. Hence, this paper introduced encryption by using RNS to improve the security of transmitted data. Traditional moduli set set {𝑚1=2𝑛−1,𝑚2= 2𝑛 𝑎𝑛𝑑 𝑚3=2𝑛+1} and Chinese Remainder Theorem (CRT) were used to encrypt and decrypt the data based on information exchange. Furthermore, this is done by representing each character in the plaintext data is assigned a unique number value and which is done by converting each character into its ASCII value and the ASCII value is then converted to Excess-3 value. Thus, anytime an attacker tries to access the data, the RNS algorithm will generate residues data, this helps to discourage an attacker from further threat. The result showed that the proposed system was able to resist brute-force attacks compared to others systems. Values of n for the moduli set were dynamic against encryption and decryption of data.
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    Machine Learning Approach Using KPCA-SVMs for Predicting COVID-19
    (Springer Nature Switzerland AG 2022, 2022-07-22) Akeem Femi Kadri; Micheal Olaolu Arowolo; Sanjay Misra
    The world has met numerous epidemics in the past decades. Lately, a deadly sickness identified as COVID-19 has surfaced from China [1, 2]. Inimitable public health adversity is triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [2–4]. The World Health Organization (WHO) termed the new epidemic as COVID-19. It is acknowledged as a Public Health Emergency of International Concern since the beginning of 2020. It was considered an epidemic around the first quarter of 2020 [5–7], as Americans joined forces with investigative institutions and scientific concerns for global artificial intelligence (AI) investigators’ activities in evolving groundbreaking machine learning measures that will help tackle COVID-19 linked surveys [3]. COVID-19 is a novel solitary intelligent ribonucleic acid (RNA) germ comprising of a huge pathological genomic sequence. It alters advances fast with no specific limitation for evaluating or investigating techniques or suitable medications. Secluding infected individuals by quarantining is the utmost way of safeguarding the universe from more escalation of the deadly COVID-19 [8].