Research & Archive

The rapid growth of cybersecurity challenges demands faster and stronger cryptographic algorithms for text and image encryption. This study introduces a new encryption algorithm based on the concept of the Fermat theorem and the use of trigonometric functions and transformations and the current cryptographic tools. The proposed approach will improve the security of data, as it is a more robust approach to saving sensitive information that combines the characteristics of transcendental number and trigonometric transformations. A prime number that is congruent to one, modulo four defines two secret keys which Fermat has established in his Theorem. The keys are then applied during the trigonometric transformation phases of encryption process. The protocol uses the decimal representation of a transcendental number to encode the plaintext characters to unique numerical values adding resistance to frequency and brute force attacks. Tangent and arctangent functions are trigonometric transformations that are used in both encrypting and decrypting the messages, which transform the integers into floating point numbers, and bring great non-linearity in the communication medium. The characteristics of trigonometric function and transcendental number lay a mathematically sound system guaranteeing a high entropy and accurate reversibility. Experimental verification spells out that the decryption protocols recover the original secret message with perfect fidelity, which makes the accuracy and reliability of the proposed method.

Speaker identification using audio data is quite challenging because of inherent differences between people, ambient noise, and variable recording conditions. Although the classical deep learning methods are effective, they have rather high computational cost, which leads to usually cumbersome parameter tuning processes and hence reduce their applicability to real-world deployments. In this paper, we discuss an efficient version of Kolmogorov-Arnold Network for audio-based speaker identification using three different types of audio inputs: bone conduction, air conduction, and throat microphone recordings, alongside evaluation on the publicly available LibriSpeech dataset to enhance reproducibility. To optimize the performance of these modalities, we investigate data-level, feature-level, and decision-level fusion techniques. An exhaustive comparison is performed against some of the prominent deep learning architectures, namely VGG-19, ResNet-152, EfficientNetV2, and DenseNet-121, both as standalone architectures and within multimodal combinations for the purpose of evaluating the proposed framework. We demonstrate that our model achieves a significant increase in accuracy up to 99.375% through the use of MFCC and GFCC feature inputs with data-level fusion through MFCC features and achieves this level of accuracy using substantially fewer parameters than those of conventional deep learning models. This makes the KAN framework a highly efficient choice for audio-based speaker identification. One of the critical innovations within this design is a class of trainable activation functions that eliminate the need for traditional weight parameters, thereby providing scalable and computationally efficient solutions. This work contributes to the field of biometrics by providing a refined, resource-efficient approach to speaker identification based on multimodal audio data.

This paper focuses on the adoption of biometric and RFID security gadgets as innovative solutions for enhancing door lock systems. The traditional reliance on physical keys has proven vulnerable to security breaches, prompting the need for more robust measures. Biometric features such as Fingerprint, Voice and Bluetooth offer unparalleled security by leveraging unique biological characteristics for authentication. Additionally, integrating RFID technology enables convenient access control through assigned cards or tags, eliminating the need for physical keys or complex passwords. The combination of these cutting-edge solutions establishes a comprehensive security infrastructure, significantly reducing risks associated with conventional lock systems. This research highlights the benefits and applications of these technologies in various settings, emphasizing their role in creating a safer environment for individuals and organizations.