Secure Timing Advance Based Context-Aware Handover Protocol for Vehicular Ad-Hoc Heterogeneous Networks

Nyangaresi, Vincent Omollo; Abeka, Silvance O.; Rodgrigues, Anthony J.

doi:10.17781/p002450

Cited by 8 publications

(5 citation statements)

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“…This is through the identification of individuals at high risk of HIV and low conception on HIV. In recent years, the use of predictive models [72]- [76] has helped in the study of prevention, diagnosis and treatment of the HIV/AIDS epidemic. This is facilitated by coming up with new aspects that would be more efficient in treatment and management of the epidemic.…”

Section: Machine Learning For Prep and Hiv Predictionmentioning

confidence: 99%

Machine learning and pre-exposure prophylaxis: A survey

Judith N. Nyakanga

2023

GSC Biol. Pharm. Sci.

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The goal of this paper was to study how machine learning techniques have been applied in PrEP (Pre-exposure prophylaxis) and HIV prediction to identify individuals who are at a higher risk of acquiring HIV infection and to optimize the used of PrEP, which is an effective method of preventing HIV transmission. The results indicate that machine learning has been used to HIV risk, optimizing PrEP use, developing personalized PrEP regimens, and identifying PrEP candidates. In predicting HIV risk, machine learning algorithms have been developed to predict HIV risk based on various factors such as demographic information, sexual behavior and drug use. The models can identify individuals who are at a higher risk of acquiring HIV infection and can be used to target interventions such as PrEP to those who are most in need. Regarding optimizing PrEP use, machine learning has been utilized to optimize PrEP usage by identifying the factors that are associated with adherence to PrEP. These models can help healthcare providers to tailor their interventions to promote PrEP adherence and improve its effectiveness. In addition, machine learning techniques have been used to develop personalized PrEP regimens based on an individual’s HIV risk profile. These models can help healthcare providers to optimize PrEP use and reduce the risk of HIV transmission. It was also established that machine learning models have been used to identify individuals who are most likely to benefit from PrEP. These models can help healthcare providers to target PrEP interventions to those who are likely to benefit from them.

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Section: Machine Learning For Prep and Hiv Predictionmentioning

confidence: 99%

Machine learning and pre-exposure prophylaxis: A survey

Judith N. Nyakanga

2023

GSC Biol. Pharm. Sci.

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“…Machine learning techniques facilitate the integration of diverse data sources, such as clinical data, imaging data, and genetic information [77]- [81]. By combining information from different modalities, these algorithms can leverage the complementary nature of the data to improve diagnostic accuracy and provide a more comprehensive understanding of the disease.…”

Section: Integration Of Multiple Data Sourcesmentioning

confidence: 99%

Survey and comparative analysis of machine learning algorithms for breast cancer diagnosis: A comprehensive review

Maurice Martin Obare

2023

World J. Adv. Res. Rev.

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Breast cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. Machine learning algorithms have emerged as powerful tools for analyzing complex medical data and aiding in the diagnosis of breast cancer. This paper provides an overview of the application of machine learning algorithms in breast cancer diagnosis. The findings indicate that machine learning algorithms, such as support vector machines (SVM), random forests, artificial neural networks (ANN), and deep learning models, have been extensively explored for breast cancer diagnosis. These algorithms leverage the vast amounts of available data, including patient demographics, medical history, imaging data (mammography, ultrasound, MRI), and genetic profiles, to identify patterns and make predictions. One of the primary applications of machine learning algorithms in breast cancer diagnosis is the classification of tumors as malignant or benign. By training on labeled datasets, these algorithms can learn to differentiate between cancerous and non-cancerous cases, thus assisting in accurate tumor diagnosis. Additionally, machine learning algorithms can be used to predict the likelihood of cancer recurrence, which helps guide treatment decisions and post-treatment monitoring. Feature selection and extraction techniques also play a vital role in breast cancer diagnosis using machine learning algorithms. These techniques aim to identify the most relevant features or biomarkers associated with breast cancer, reducing the dimensionality of the data and enhancing the performance of the models. Feature selection algorithms, such as recursive feature elimination and correlation-based feature selection, contribute to the identification of critical indicators for accurate diagnosis. Furthermore, the integration of different data sources and modalities, such as combining clinical data with imaging data or genetic data, has shown promise in improving breast cancer diagnosis accuracy. By fusing multiple types of information, machine learning algorithms can leverage the complementary nature of these data sources to enhance diagnostic capabilities. Despite the advancements made, challenges remain in the field of breast cancer diagnosis using machine learning algorithms. Issues such as data quality, interpretability of models, and generalizability to diverse populations need to be addressed to ensure the reliable and equitable application of these algorithms in clinical practice.

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“…Due to parallelism in their design, quantum algorithms are able to solve problems inherent in classical algorithms, such as speed and space. As Martina et al [50] explain, quantum computations exhibits promising applications in machine learning and data analysis with much more advance in time and space complexity [51] . The entanglement property in quantum computing provides a mechanism for improving the storage capacity as well as retrieving corrupted or incomplete information [52] .…”

Section: Consumes Timementioning

confidence: 99%

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Arika

Mindila

Cheruiyo

2022

Jour. of oncol. res.

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Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.

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Secure Timing Advance Based Context-Aware Handover Protocol for Vehicular Ad-Hoc Heterogeneous Networks

Cited by 8 publications

References 0 publications

Machine learning and pre-exposure prophylaxis: A survey

Machine learning and pre-exposure prophylaxis: A survey

Survey and comparative analysis of machine learning algorithms for breast cancer diagnosis: A comprehensive review

Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

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