Enhancing cancer diagnosis accuracy with a hybrid ML model: A study on UAE patient data

The beamforming approach has been emerging as a very important concept for next generation networks. In addition to the improved channel capacity, spectral efficiency, energy efficiency, secrecy rate and secrecy outage probability, the upcoming fifth generation network mainly aims at enhancing the parameters of the channel for secure communication. In this paper, we have implied the allocation of resource blocks adaptively using HMM with a beamforming approach in an intruded network. A system model for secure communication in an intruded network has been discussed using a beamforming approach with the main motive being to provide a security scenario to the data which is transmitted over an unsecured channel in a network. In addition to this we have used the approach of HMM for allocating the resource blocks to the users which have been demanded and applied in order to avoid the intrusion and wastage of resource blocks.

Design and implementation of novel 6G cellular communication architecture using 3D modelling in real environments

Academics and businesses alike are presently fixated on the Sixth Generation (6G) network, which is seen as the telecom industry’s next major game-changer. The 6G architecture has not been finalized and is not yet being used in commercial settings. Research and development for 6G is still in its early stages. Several important features and technologies have surfaced as possible 6G system underpinnings, while development is still in its early phases. To facilitate the next generation of 3D modeling applications, this article suggests a new 6G cellular communication architecture. Aware of 6G’s revolutionary potential, we investigate its fundamental features to build a collaborative, real-time 3D modeling environment with unmatched capabilities. The goal of this project is to design the architecture for the next generation of communications systems. This will incorporate elements from two existing designs: the decoupled RAN design, which improves security and smooth data sorting, and the high-level design, which incorporates numerous protocols inside the antenna for stringent protection. Lastly, we delve into the possible sector-specific disruptions caused by this design and examine its wider implications for the future of 3D modeling. We hope that by introducing this new 6G architecture, we may inspire more study and development towards a day when 6G technology completely changes the game when it comes to 3D modeling.

Enhancing cancer diagnosis accuracy with a hybrid ML model: A study on UAE patient data

Esraq Humayun, Farzana Talukder Sumona, Lim Kok Cheng, Md Shahin Hossain, Ali Selamat, Ondrej Krejar

Article ID: 8228
Vol 3, Issue 2, 2025

DOI: https://doi.org/10.54517/cte8228

Received: 25 May 2025; Accepted: 1 June 2025; Available online: 18 June 2025; Issue release: 30 June 2025

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Abstract

Preliminary identification of cancer is still essential because it can greatly improve the chances of survival of a patient, as cancer is the leading reason of death internationally. In this study, we introduce a mixed machine learning (ML) model using support vector machines (SVM) and random forest (RF) algorithms. To improve accuracy in diagnosing cancer, classifiers such as linear regression (LinReg), support vector machines (SVM), and logistic regression (LogReg) are used. This way, the model is tested and validated using the UAE Cancer. It contains medical records of all patients, their demographic information, clinical information, and outcomes. Our results demonstrate that the hybrid model achieved 98.3% accuracy, 98.5% recall, and 0.99 AUC-ROC, outperforming individual classifiers. Policies are justiciable despite the difficulties of needing to validate findings from many datasets, make them easy to use clinically, and manage biases in the available information. Because of this study, people are considering how hybrid ML models can be beneficial in clinical care and are encouraging more research on cancer diagnostics.

Keywords

cancer diagnosis; machine learning; hybrid model; support vector machine; random forest; logistic regression; UAE cancer dataset; accuracy; recall; AUC-RO

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