Dynamic Spectrum Sharing is the Best Way to Modify Spectrum Resources

Mahmood, Zuhair Shakor; Coran, Ali Nasret Najdet; Kamal, Ayoub Esam; Noori, Abbas B.

doi:10.1109/asiancon51346.2021.9544912

Cited by 8 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main advantage of this algorithm is that it can process the numerical and categorical data. It's considered to be efficient in the case of classification and regression algorithms and for all data sizes [19].…”

Section: And DL Modelsmentioning

confidence: 99%

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Yassen,

Abdulrazzq,

Mohammed

2023

ISI

View full text Add to dashboard Cite

The Internet of Things (IoT) has become an integral component in various applications, with significant prominence in healthcare and cybersecurity sectors. It is indispensable in medical diagnostics, monitoring, decision-support systems, and the safeguarding of sensitive data. However, the traditional methodologies have shown limitations in their ability to detect and classify all types of attacks effectively. This study presents a robust feature selection model, ANOVA-Recursive Feature Elimination (ANOVA-RFE), implemented with both Machine and Deep Learning paradigms, aiming to augment the security level by enhancing attack detection and classification. The models were trained using both the entire feature set and the selected features identified by ANOVA-RFE, demonstrating the efficiency and precision of the proposed method. The experiments yielded an accuracy of 100% and 99.96% using only the top five selected features from the first and second datasets, respectively. Furthermore, the performance of Gaussian Naive Bayes (GNB), K-Nearest Neighbors (K-NN), Random Forest (RF), AdaBoost (AB), Logistic Regression (LR), Decision Tree (DT), and Long Short-Term Memory (LSTM) models are evaluated, showcasing their respective accuracies on the first dataset. A scorelevel fusion was also employed, and the results were benchmarked against the current stateof-the-art, validating the robustness and high precision of the current study. Future work should consider analyzing different datasets and addressing further challenges.

show abstract

Section: And DL Modelsmentioning

confidence: 99%

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Yassen,

Abdulrazzq,

Mohammed

2023

ISI

View full text Add to dashboard Cite

show abstract

“…Thin metal films patterned with nanostructures may also be used for surface plasmon resonance (SPR), making them a promising material with which to create filters. Depending on the nanostructures of the metal sheet, the incident light links with the surface plasmon in a certain manner [3,4,5]. By altering the nanostructures, the resonant wavelength may be tuned, allowing the desired light filtration to be achieved.…”

Section: Introductionmentioning

confidence: 99%

“…were shown to function in the visible spectrum. Electron beam lithography (EBL) was used to manufacture the aluminum nanostructures that made up the filter on the surface of the plasmonic CIS (pCIS), which coated an area of the photodiode that was around 1 mm 2 [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

A New Filter Design Built Into CMOS Image Sensor

Mahmmod¹,

Najdet²,

Noori³

2023

JIPIRS

Self Cite

View full text Add to dashboard Cite

dry etch and Electron beam lithography were used to directly integrate plasmonic color filters onto the top surface of a complementary metal oxide semiconductor (CMOS. The filters have a spectral range of 3.5 × 8 microns. After measuring the photocurrent, it was found that the 150×150-pixel plasmonic CIS was sensitive to all colors in the visible spectrum. The filters were made using a straightforward method that only required a single lithographic procedure. This is in contrast to the standard multi-stage processing required when working with dyedoped polymers. It is possible to fabricate these plasmonic components on a metal layer near to the photodiodes since they are intrinsically compatible with a typical CMOS process. Future low-cost, low-cross-talk, high-functionality advanced CIS may be possible with the help of such plasmonic components.

show abstract

“…A biometric sample is collected, analyzed, and saved for comparison later on during registration. A person's claimed identity may be verified or authenticated using recognition in either identification or identification mode (the system determines the best match from the full enrolled population) are examples of this [6], [7]. Since the 1970s, techniques of speaker recognition have been deemed "classic" in the field of biometrics [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…This article makes advantage of a previously developed continuous speech recognizer to construct an autonomous surface vehicle (ASV) system that relies on text dependence, high quality speech and cooperative speakers. To verify a speaker's identity, the process usually involves a few simple steps: the claimant speaks a phrase into a microphone, which is picked up by the system, which decides whether to reject or accept or wither to report a lack of confidence or reject the claim and ask for more speech before making a decision [7], [20].…”

Section: Introductionmentioning

confidence: 99%

Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

Kadhim

Nasret²,

Mahmood³

2022

IJEECS

Self Cite

View full text Add to dashboard Cite

<div class="WordSection1"><p>The purpose of this study is to discuss the design and implementation of autonomous surface vehicle (ASV) systems. There’s a lot riding on the advancement and improvement of ASV applications, especially given the benefits they provide over other biometric approaches. Modern speaker recognition systems rely on statistical models like hidden Markov model (HMM), support vector machine (SVM), artificial neural networks (ANN), generalized method of moments (GMM), and combined models to identify speakers. Using a French dataset, this study investigates the effectiveness of prompted te xt speaker verification. At a context-free, single mixed mono phony level, this study has been constructing a continuous speech system based on HMM. After that, suitable voice data is used to build the client and world models. In order to verify speakers, the text-dependent speaker ver-ification system uses sentence HMM that have been concatenated for the key text. Normalized log-likelihood is determined from client model forced by Viterbi algorithm and world model, in the verification step as the difference between the log-likelihood. At long last, a method for figuring out the verification results is revealed.</p></div>

show abstract

Dynamic Spectrum Sharing is the Best Way to Modify Spectrum Resources

Cited by 8 publications

References 9 publications

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

A New Filter Design Built Into CMOS Image Sensor

Enhancement and modification of automatic speaker verification by utilizing hidden Markov model

Contact Info

Product

Resources

About