INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY en-US (Editorial Office) (Manbir Singh) Sun, 28 Jan 2024 05:56:19 +0000 OJS 60 Redundant Residue Number System (RRNS) Di-Base Table for SOLiD Sequencing <p><span style="font-weight: 400;">The next-generation sequencing (NGS) methodology, sequencing by oligonucleotide ligation and detection (SOLiD) uses a di-base table, a somewhat unusual method, to decode sequences. Its coding scheme is based on the binary number system. The di-base table is not connected to the genetic code, nor is the coding scheme structured in the space of an entire number system. Gamow also revealed the hidden attribute of a </span><span style="font-weight: 400;">4 × 4 </span><span style="font-weight: 400;">code for the di-base table, supporting his proposal of a </span><span style="font-weight: 400;">4 × 4 × 4 </span><span style="font-weight: 400;">codons for the genetic code. Consideration for digital applications has focused more on the Residue Number System (RNS) and Redundant Residue Number System (RRNS) lately. Consequently, an RRNS di-base table based on the number tree concept is designed. The designed RRNS di-base table deviates from the canonical di-base table but retains every attribute necessary for effective SOLiD decoding. It shares a close relationship with the RNS-Genetic code and this presents a compelling argument for creating a single instrument that possesses the capabilities of both the genetic code and the di-base table.</span></p> Joshua Apigagua Akanbasiam, Kwame Osei Boateng, Matthew Glover Addo Copyright (c) 2024 Joshua Apigagua Akanbasiam, Kwame Osei Boateng, Matthew Glover Addo Mon, 17 Jun 2024 00:00:00 +0000 An EEG-based Sleep Staging method with hybrid entropy computation measures <p>Sleep is an indispensable physiological need of the human body. Sleep staging is an effective method to objectively assess sleep quality and is helpful for research on sleep and sleep-related diseases. Electroencephalogram (EEG) signals are nonlinear and non-stationary time series, and entropy features are particularly sensitive to these nonlinear characteristics and can reveal information that is difficult to discover with traditional linear analysis methods. We proposed an automatic sleep staging method based on EEG entropy computation, inlcuding signal preprocessing, entropy feature extraction, feature selection and lassification modules. The experimental results show that the average accruacy is 91.3% through the fused entropy features.</p> Yiqian Yang, Shuchen Fu, Ruixiang Liao Copyright (c) 2024 Yiqian Yang, Shuchen Fu, Ruixiang Liao Mon, 17 Jun 2024 00:00:00 +0000 Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration <p>The concept of consciousness levels typically refers to various aspects and tiers related to an individual’s cognition, perception, thinking, and awareness. Although neurophysiological markers have not yet been definitively identified to distinguish between these nuanced levels, this paper introduces a robust marker, the Approximate Entropy (ApEn), which quantifies the complexity of EEG signals to differentiate states of altered consciousness. Utilizing ApEn, we analyze EEG data from the frontal lobe—a region closely associated with consciousness—in states indicative of severely altered conditions, specifically anesthesia, coma, and brain death. To enhance the precision of consciousness level<br />assessment, we employ a Support Vector Machine (SVM) model, which classifies the states based on EEG complexity measures. This approach not only provides valuable insights into the neural correlations associated with changes in these critical states but also underscores the potential of combining quantitative EEG analysis with machine learning techniques to advance our understanding of consciousness. The findings demonstrate that EEG complexity, when analyzed using ApEn coupled with SVM classification, offers a novel and effective method for assessing and distinguishing between degrees of consciousness. This approach promises significant implications for clinical diagnostics and patient monitoring.</p> Jingming Gong, Linfeng Sui, Ran Zhang, Boning Li, Chengyuan Shen, ,Taiyo Maeda, Jianting Cao Copyright (c) 2024 Jingming Gong, Linfeng Sui, Ran Zhang, Boning Li, Chengyuan Shen, ,Taiyo Maeda, Jianting Cao Wed, 05 Jun 2024 00:00:00 +0000 A NEW ROBUST HOMOMORPHIC ENCRYPTION SCHEME BASED ON PAILLIER, RESIDUE NUMBER SYSTEM AND EL-GAMAL <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>The new focus of cryptographic research is on encryption schemes that can withstand cyber-attacks, with the arrival of cloud computing. The widely used public key encryption system designed by Taher El Gamal based on the discrete logarithm problem has been used in many sectors such as internet security, E-voting systems, and other applications for a long time. However, considering the potential data security threats in cloud computing, cryptologists are developing new and more robust cryptographic algorithms. To this end, a new robust homomorphic encryption scheme based on Paillier, Residue Number system (RNS), and El Gamal (PRE), is proposed in this paper., which is expected to be highly effective and resistant to cyber-attacks. The proposed scheme is composed a three-layer encryption and a three-layer decryption processes thereby, making it robust. It employs an existing RNS moduli set {2n + 1, 2n, 2n − 1, 2n−1} − 1}, having passed it through the Paillier encryption process for forward conversion and then the El Gamal cryptosystem to encrpyt any data. The decryption process is a reversal of these processes starting from the El Gamal through a reverse conversion with the same moduli set using the Chinese Remainder Theorem (CRT). The simulation results shows that the proposed scheme outperforms similar existing schemes in terms of robustness and therefore, making it more secured which however, trades off with the time of execution in similar comparison.</p> </div> </div> </div> Peter Awonnatemi Agbedemnab, Abdul Somed Safianu and, Abdul-Mumin Selanwiah Salifu Copyright (c) 2024 Peter Awonnatemi Agbedemnab, Abdul Somed Safianu and, Abdul-Mumin Selanwiah Salifu Wed, 17 Apr 2024 00:00:00 +0000 On Defining Smart Cities using Transformer Neural Networks <p><span style="font-weight: 400;">Cities worldwide are rapidly adopting “smart” technologies, transforming urban life. Despite this trend, a universally accepted definition of “smart city” remains elusive. Past efforts to define it haven’t yielded a consensus, as evidenced by the numerous definitions in use. In this paper, we endeavored to create a new “compromise” definition that should resonate with most experts previously involved in defining this concept and aimed to validate one of the existing definitions. We reviewed 60 definitions of smart cities from industry, academia, and various relevant organizations, employing transformer architecture-based generative AI and semantic text analysis to reach this compromise. We proposed a semantic similarity measure as an evaluation technique, which could generally be used to compare different smart city definitions, assessing their uniqueness or resemblance. Our methodology employed generative AI to analyze various existing definitions of smart cities, generating a list of potential new composite definitions. Each of these new definitions was then tested against the pre-existing individual definitions we’ve gathered, using cosine similarity as our metric. This process identified smart city definitions with the highest average cosine similarity, semantically positioning them as the closest on average to all the 60 individual definitions selected.</span></p> Andrei Khurshudov Copyright (c) 2024 Andrei Khurshudov Sun, 28 Jan 2024 00:00:00 +0000 Convolutional Neural Networks for Deep Sleep Detection Based on Data Augmentation <p>Sleep is a necessary process that individuals undergo daily for physical recovery, and the proportion of deep sleep in the sleep stages is a critical aspect of the recovery process. Convolutional Neural Networks (CNNs) have shown remarkable success in automatically identifying deep sleep stages through the analysis of electroencephalogram (EEG) signals. This article introduces three data augmentation techniques, including time shifting, amplitude scaling and noise addition, to enhance the diversity and features of the data. These techniques aim to enable machine learning models to extract features from various aspects of sleep EEG data, thus improving the model’s accuracy. Three deep learning models are introduced, namely DeepConvNet, ShallowConvNet and EEGNet, for the identification of deep sleep. To evaluate the proposed methods, the Sleep-EDF public dataset was utilized. Experimental results demonstrate that the enhanced dataset formed by applying the three data augmentation techniques achieved higher accuracy in all deep learning models compared to the original dataset. This highlights the feasibility and effectiveness of these methods in deep sleep detection.</p> Ruixuan Chen, Linfeng Sui, Mo Xia, Jinsha Liu, Tao Zhang, Jianting Cao Copyright (c) 2024 Ruixuan Chen, Linfeng Sui, Mo Xia, Jinsha Liu, Tao Zhang, Jianting Cao Sun, 28 Jan 2024 00:00:00 +0000