e- GCloud: Next Wave of E-Government

Omer K. Jasim; Mohammed E.; Osamah M.

doi:https://doi.org/10.54216/FPA.170104

Journal of Intelligent Systems and Internet of Things | 2024

A Hybrid Intelligence-based Deep Learning Model with Reptile Search Algorithm for Effective Channel Estimation in massive MIMO Communication Systems

Channel estimation poses critical challenges in millimeter-wave (mmWave) massive Multiple Input, Multiple Output (MIMO) communication models, particularly when dealing with a substantial number of antennas. Deep learning techniques have shown remarkable advancements in improving channel estimation accuracy and minimizing computational difficulty in 5G as well as the future generation of communications. The main intention of the suggested method is to use an optimal hybrid deep learning strategy to create a better channel estimation model. The proposed method, referred to as optimized D-LSTM, combines the power of a deep neural network (DNN) and long short-term memory (LSTM), and the optimization process involves the integration of the Reptile Search Algorithm (RSA) to enhance the performance of  deep learning model. The suggested hybrid deep learning method considers the correlation between the measurement matrix and the signal vectors that were received as input to predict the amplitude of the beam space channel. The newly proposed estimation model demonstrates remarkable superiority over traditional models in both Normalized Mean-Squared Error (NMSE) reduction and enhanced spectral efficiency. The spectral efficiency of the designed RSA-D-LSTM is 68.62%, 62.26%, 30.3%, and 19.77% higher than DOA, DHOA, HHO, and RSA. Therefore, the suggested system provides better channel estimation to improve its efficiency.

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Nallamothu Suneetha mail -

Penke Satyanarayana mail

link https://doi.org/10.54216/JISIoT.130227

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Neutrosophic and Information Fusion | 2024

The basis number of connected vertex-disjoint graphs

The basis number b (G) of a graph G is defined to be the smallest positive integer k such that G has a k-fold basis for its cycle space. We try to find an upper bound for b (G_1+G_2+G_3+G_4). We prove that, if G_1,G_2,G_3 and G_4 are connected vertex-disjoint graphs and each has a spanning tree of vertex degree not more than 4, then b(G_1+G_2+G_3+G_4)≤max{4,b(G_1)+1,b(G_2)+2,b(G_3) +2,b (G_4)+1}. The basis number of quadruple join of paths will be studied, where we prove that b p_m+ p_n+p_p+p_t) =4, ∀m,t≥5  and n,p≥6.

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Barbara Charchekhandra mail

link https://doi.org/10.54216/NIF.040101

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2024

On the Computation of Units in Symbolic 4- Plithogenic Ring of Integers

In this paper, we study the invertible elements (units) in the symbolic 4-plithogenic ring of integers, where we use a computational algorithm to find all units in the mentioned ring. The all-elements group of 32 symbolic 4-plithogenic ring of integers has been found and listed.

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Lee Xu mail

link https://doi.org/10.54216/NIF.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Neutrosophic and Information Fusion | 2024

Design and Implementation of Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme

Images may be protected from hackers and attackers with the use of steganography. The rapid expansion of the internet has led to the widespread distribution of vast quantities of multimedia content, including photos, movies, and audio files, via various online platforms. To ensure the safety of sensitive information while it is in transit and upon receipt, a high degree of security is required. During the patient scanning procedure, hospitals and scan centers save many pictures of patients on personal computers. Protection from strangers who may see the patients' scanned photos would be necessary for this. Therefore, scan centers and hospitals all over the globe rely heavily on medical image security. The proposed technique includes Fuzzy Logic-Based Key Exchange Protocol in Medical Image Cryptographic Protection Scheme.  To provide the utmost protection for the medical pictures, the cover image incorporates the secret image. At the outset, we standardize the cover and hidden photos. The cover image for this thesis might be a picture of nature or a benchmark; the hidden image, on the other hand, is a medical image in grayscale or binary format. After that, the normalized picture is processed using DWT. The hidden picture is embedded into the cover image using a fuzzy-based edge-related steganography approach, which uses these altered coefficients. To get the stego image, the embedded picture is normalized in the reverse direction. Additionally, this study suggests DT-CWT transform based picture security. Part one of the suggested approach to picture security is image steganography, and part two is picture cryptography. Module 1 uses the DT-CWT transform to fuse the coefficients of the cover picture with the hidden image. After that, the steganography picture is subjected to module 2, which is based on the IE calculation. Analysis of experimental data for the suggested picture security approach revealed improved outcomes for encrypted communication.

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Erina Kovachiskaya mail

link https://doi.org/10.54216/NIF.040104

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Neutrosophic and Information Fusion | 2024

Differentially Private Clustering with Dynamic Noise Adjustment (DPC-DNA) based Fusion Anonymity and Privacy Enhancement in Big Data

Other few challenges faced during privacy preservation by anonymity e.g. difficulty in identifying the The main challenges in preserving anonymity for privacy are determining which attributes could undermine privacy and extracting useful information from massive databases without disclosing sensitive details. We developed a Novel Framework for Differentially Private Clustering with Dynamic Noise Adjustment (DPC-DNA) that addresses these issues. This novel approach can recognize sensitive and non-sensitive data aspects using Differentially Private Clustering with Dynamic Noise Adjustment (DPC-DNA). The accuracy of clusters formed by DPC-DNA was assessed using the silhouette score, which gauges how similar each item is to its own group versus others. DPC-DNA achieved a silhouette score of 0.62, signalling strong internal cluster composition. In contrast, traditional k-anonymity clustering yielded a lower score of 0.45, confirming that DPC-DNA significantly boosts accuracy. Our Novel Framework for Differentially Private Clustering with Dynamic Noise Adjustment (DPC-DNA) provides a robust solution for privacy-preserving data mining. By combining differential privacy with adaptive noise management, it safeguards sensitive material while sustaining high precision, integrity and usefulness of results.

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Sergey Drominko mail

link https://doi.org/10.54216/NIF.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

An Intelligent IDS for Mobile Adhoc Networks using Differential Evolutionary and Navie Bayesin Algorithms

Ad-hoc Networks are structure less, auto-designing, self mending and dynamic in nature. The manet geography which are more helpless to have security issues and clearly self important to different kinds of assaults. The IDS framework has been created in manet to address the different assaults in Ad-hoc networks. Irregularity interruption recognition is bothered with ready to distinguishing occasions that give off an impression of being confused assaults. In contrast to single and gathering of nodes, causes assaults may cause all the more destroying impacts on remote conditions. To guard against different shared assaults. In this paper, we propose 'An Intelligent IDS  for mobile adhoc network using Differential Evolutionary and Navie Bayesian algorithm (DEANB)‘ calculation.  The proposed framework is for the most part centers to identify and forestall the malevolent node in Ad-hoc organizes and arrange the believed node utilizing the NB  idea and node choice is upgraded utilizing DE calculation. This proposed framework which likewise diminishes the bogus positive pace of Ad-hoc nodes and expands the reliability of the node took part in dynamic systems. The proposed framework can identify wormhole, dark opening, flooding and specific bundle drop and furthermore builds the exhibition of system as far as various boundaries like throughput, directing over-head, start to finish postponement and packet conveyance proportion, and so forth. In this way the recreations in NS-2 shows that the proposed framework has impressively diminishes the vindictive trouble making of nodes in networks.

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P. Maheswaravenkatesh mail -

K. Nithya mail -

V. Kandasamy mail -

R. Kiruba buri mail -

A. Sumaiya Begum mail

link https://doi.org/10.54216/JCIM.150105

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

A Transformer-Enhanced System to Reverse Dictionary Technology

The ability to retrieve a word from the cusp of memory often encounters the well-documented Tip-of-the-Tongue (TOT) barrier. This cognitive phenomenon can impede communication and learning. Addressing this, our study introduces a novel reverse dictionary framework empowered by cutting-edge neural network architectures to facilitate the retrieval of words from definitions or descriptions. This research draws the path of the development and the efficiency of various natural language deep learning models formulated to grasp the semantics inside the text. This work started with gripping a new dataset with rich content from a linguistic perspective. An accurate pre-processing step, including text normalizations and contextual features extraction, was conducted to transform the unstructured text into structured features fitting the model training. Dense vectors representative of text have been extracted using the BERT embedding model. Three models (LSTM, FNN, and GRU) were tested and compared using scrapped and benchmarked data. The proposed model that was consisted from Bert embedding and LSTM learner was evaluated and showed notable performance under cosine similarity and mean square error metrics. The LSTM model proved useful in real-world applications by exhibiting excellent semantic coherence in its embedding and accuracy in its predictions. This research evolved a discussion about the efficient behavior of the pre-trained BERT model in enhancing vocabulary. In addition, this work sheds light on the crucial role of reverse dictionaries in many NLP applications in the future. Subsequent research endeavors will focus on augmenting the multilingual functionalities of our methodology and investigating its suitability for other cognitive linguistic phenomena.

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Ahmed Bahaaulddin A. Alwahhab mail -

Vian Sabeeh mail -

Ali Sami Al-Itbi mail -

Ali Abdulmunim Ibrahim Al-kharaz mail

link https://doi.org/10.54216/FPA.170101

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Screening Epileptic Seizures in EEGs Using Interictal Epileptiform Discharge Waveforms and Convolutional Neural Networks

The integration of Artificial Intelligence (AI) within the Medical Internet of Things (MIoT) is advancing swiftly, leading to significant developments in the detection of illnesses like epilepsy by analyzing Interictal Epileptiform Discharges (IED) in electroencephalograms (EEG).The availability of EEG data has facilitated the creation of innovative applications, including seizure detection. While neurologists have traditionally relied on EEG data analysis to identify epileptic seizures, the manual evaluation of EEG brain waves is a laborious and complex process that places significant stress on specialists. This paper presents a simple Convolutional Neural Network (CNN) method for the automated detection of IEDs based on EEG waveforms. This approach helps reduce the burden on epilepsy patients by forecasting seizures and enabling timely interventions. It also eases the workload for neurologists and less experienced specialists, thereby accelerating the diagnosis process. The proposed method was implemented by utilizing a series of images that depicted the magnitude of the EEG signal across each sensor. The study divided participants into two groups: (A) healthy individuals and (B) individuals with epilepsy. The results demonstrated an accuracy of up to 96.4% compared to human expert diagnoses, displaying the method's effectiveness and practicality in detecting seizure occurrences in EEG data.

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Mahy E. Elemam mail -

A. F. Elgamal mail -

I. Elmenshawi mail -

Hanan E. Abdelkader mail

link https://doi.org/10.54216/FPA.170102

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Egyptian Bitcoin Investments: A Comprehensive Examination of Investor Sentiment Effects on Bitcoin Returns

This research investigates how Egyptian investor sentiment affects cryptocurrency returns, focusing specifically on Bitcoin. We utilized an enhanced investor sentiment index in Egypt, constructed through factor analysis of various literature-based variables. Our study's findings revealed a notable positive correlation between the investor sentiment index, lagged by one order, and Bitcoin returns, as per the estimation and analysis using VAR models. Analysis indicates that a one standard deviation change in the investor sentiment index leads to an alteration in the influence of each standard deviation of the original positive variable, resulting in a switch from positive to negative and vice versa in the medium and long term. Regarding variance decomposition, the short-term variance error of 100% is primarily explained by Bitcoin returns themselves. However, in the medium to long term, besides Bitcoin returns, the investor sentiment index emerges as the most influential variable affecting Bitcoin returns. Causality tests reveal a unidirectional short-term impact from the investor sentiment index to Bitcoin returns via Granger causality tests. Additionally, using the Toda-Yamamoto causality test, long-term bidirectional effects between Bitcoin returns and the investor sentiment index were observed.

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Ahmed H. Elgayar mail -

Farouk F. Elgazzar mail -

Noura Metawa mail

link https://doi.org/10.54216/FPA.170103

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

e- GCloud: Next Wave of E-Government

This research delves into the developments, in cloud computing and their significance for e government. It introduces an approach to e government advancement known as "Electronic Governmental Cloud (e-GCloud) " aimed at addressing identified issues and meeting the requirements of cloud computing. The study will conduct a review of existing literature and online sources analyzing studies and articles on the evolving landscape of cloud computing to elucidate its role in e government applications. It aims to outline the deployment strategy for cloud computing in e government settings and propose a novel governmental framework called "e- GCloud” designed as an exclusive private cloud community for national governments use. Additionally, this research, evaluates factors influencing the integration of cloud computing into e government systems by drawing insights from senior government officials and IT personnel within governmental entities. The results suggest that e-GCloud outperforms in applications due, to its enhanced flexibility, resource availability and prompt responsiveness.

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Omer K. Jasim Mohammad mail -

Mohammed E. Seno mail -

Osamah M. Abduljabbar mail

link https://doi.org/10.54216/FPA.170104

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new