Classification Nutrient Deficiency of Maize Plant Leaf Using Machine Learning Algorithm

Ashish; Richa; Aditi

doi:https://doi.org/10.54216/FPA.170106

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

Details open_in_new

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

Galoitica: Journal of Mathematical Structures and Applications | 2024

On the Numerical Solutions Based on a Novel Hybrid Method for Some VNDEs Problems

This paper is devoted to find the solution of the Vanishing Neutral Differential Equations (VNDEs), where we review the ARCHI code for solving neutral differential equations, and then an improvement of this code will be presented in this paper to solve problems of VNDEs. This improvement is done by suggesting a new hybrid method of special interpolants with iteration procedure of RK method. We will analyze the convergence of the suggested new method by proving the following criteriaBn(t)≤TH(c1+(c2L3)/(1-L3 ))exp(LT/(1-L3 ))Where the solution is convergent , we have  and when and the derivative of the solution is also convergent for VNDEs.

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Stipan Podobnic mail -

Barbara Charchekhandra mail

link https://doi.org/10.54216/GJMSA.0110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

On Some Results about Complex Order Bessel's Equation

In this paper, we derive the Bessel's equation of complex order (n + i) from the classical well-known Bessel's equation. In addition, we generalize that recurrence relation from Bessel's equation of order (n) to Bessel equation of complex order (n + i). On the other hand, we present an algorithm to solve the novel complex order equation with many illustrated examples that explain the validity of our approach.

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Arwa Hajjari mail

link https://doi.org/10.54216/GJMSA.0110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

On Some W-Hosoya polynomials for Several Special Connected Graphs

Let u and v be any two distinct vertices in a connected graph G. A container C(u,v) is a set of internally disjoint u - v paths. The width of C(u,v) is denoted by w or w(C(u,v)), it is equal to , and the length of is the length of the longest u – v path in C(u,v). Then, for a given positive integer w, the width distance between any two distinct vertices u and v in a connected graph G is define by:dw (u,v)=min/(C(u,v)) l(C(u,v)) , where the minimum is taken over all containers C(u, v) of width w.In this paper, we find the Hosoya polynomials and Wiener indices of the join of two special graphs such as bipartite complete graphs, paths, cycles, star graphs and wheel graphs with respect to the width distance.

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Lee Xu, Taher mail -

Ahmed Jubbori mail

link https://doi.org/10.54216/GJMSA.0110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Proposed Ensemble Model of Network Intrusion Detection System for binary and Multiclassification

A network Intrusion detection system is a system that can find out different types of attacks. ANIDS is used to find out the noble type of attack by using machine learning and deep learning techniques. These techniques are very useful to find out those attacks whose patterns are not stored in the database. Therefore, these types of systems need more research to improve their accuracy and reduce the false alarm rate. In this paper, we are going to propose an ensemble framework for NIDS using different ML and DL techniques. In this paper, we have used the XGBOOST algorithm for feature extraction and for classification, CNN and RNN deep learning techniques are used. This ensemble model is used for the binary and multiclassification of attacks. Our model was checked on the dataset CICIDS-2018 which gives a better accuracy and low false alarm rate.

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Amrita Bhatnagar mail -

Arun Giri mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.170105

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Classification Nutrient Deficiency of Maize Plant Leaf Using Machine Learning Algorithm

The development and productivity of maize, an important crop worldwide, may be stunted by several nutritional deficiencies. If we want to increase maize output, we need to find these problems quickly. This study suggests a thorough method for identifying nutritional deficits in maize plants by analyzing leaf photos. Our approach combines deep learning algorithms with conventional machine learning methods to analyze and extract information from these pictures. The four types of nutritional deficiencies that were examined are zinc (Zn), potassium (K), nitrogen (N), and phosphorus (P). The standard machine learning method uses Gabor, Discrete Wavelet Transform, Local Binary Pattern, and Gray-Level Co-occurrence Matrix (GLCM). Then, classification is done using algorithms like Support Vector Machine (SVM), Decision Tree, and Gradient Boosting. According to our experimental data, machine-learning algorithms successfully diagnose nutritional deficits in maize plants. The results of this study highlight the promise of machine learning algorithms for improving agricultural yields via better plant nutrition management. Farmers and agricultural specialists may greatly benefit from automated image analysis that can identify nutritional deficits in maize plants quickly and correctly. This technology has the potential to contribute to the sustainability and security of food on a worldwide scale.

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Ashish Patel mail -

Richa Mishra mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.170106

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new