Delay Sensitive Protocol for High Availability LTE Handovers

Nyangaresi, Vincent Omollo; Abeka, Silvance O.; Rodrigues, Anthony

doi:10.11648/j.ajnc.202009012.11

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Researchers in [69] used machine learning and created a viable machine learning model [70] using a digital survey data with interlinking potential utility in directing health resources including PrEP towards area of great potential gain. This is by using a guided tool for assessing risk behavior to contacting HIV in South Africa.…”

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 approaches are adaptable to different datasets and can be trained on large amounts of data. As more data becomes available, the algorithms can continuously learn and adapt to improve their performance [84]- [86]. This scalability allows for the incorporation of new knowledge and the potential to refine diagnostic models over time.…”

Section: Adaptability and Scalabilitymentioning

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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“…Multimedia SMS also pose a security challenge by increasing likelihood for SMS spam through rich media including images, videos and emoji. It is therefore imperative to adopt deep learning techniques such as Recurrent Neural Networks (RNN) which process languages, images, emoji, and videos [84], [129]- [134].…”

Section: Related Workmentioning

confidence: 99%

A comparative analysis of recurrent neural network and support vector machine for binary classification of spam short message service

David Odera,

Gloria Odiaga

2023

World J. Adv. Eng. Technol. Sci.

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Over the years, communication through Short Message Service (SMS) has been a primary tool for mobile subscribers. SMS has varied applications in health, industry, finances, education and social networking among others. The growth of mobile devices and SMS usage has consequently increased the attack surface for cyber-criminals culminating to the proliferation of malicious activities introduced using SMS spam, phishing, spyware, malware etc. Ham messages are normal messages people trade with one another and are usually not unwanted by the recipient, while spam messages are unsolicited junk and redundant messages that may be sent to a large number of people at once and are usually unwanted. Various spam detection models have been developed using various traditional machine and deep learning techniques. However, most studies where comparison between deep and traditional machine learning algorithms is done, have unfortunately omitted K-Nearest Neighbors and Support Vector Machine (SVM) which are empirically deemed as the most popular traditional machine learning algorithms. In this study, therefore, we develop a deep learning model based on Recurrent Neural Network (RNN) for Spam and Ham SMS classification and compare its performance against SVM model for the same University of California (UCl) SMS dataset. The results show that RNN has a slightly higher training and validation accuracy of 0.98 compared to SVM at 0.94, however, the false positive rate of SVM is marginally lower. Exploring application of deep learning with better optimization algorithms such as RNN improves accuracy, reduces computational complexities i.e. memory consumption and speed, and thus minimizing false positive rates. For future work, we suggest the use of varied performance metrics to validate the model in a distributed dataset environment.

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Delay Sensitive Protocol for High Availability LTE Handovers

Cited by 7 publications

References 8 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

A comparative analysis of recurrent neural network and support vector machine for binary classification of spam short message service

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