Emotion Recognition System Based on Hybrid Techniques

Abdulsalam, Wisal Hashim; Alhamdani, Rafah Shihab; Abdullah, Mohammed Najm

doi:10.18178/ijmlc.2019.9.4.831

Cited by 19 publications

(8 citation statements)

References 17 publications

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“…Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated potential in diverse applications, such as predicting [7][8][9], recognizing emotions [10][11][12], creating a 3D model of an object from 2D images [13], classi cation and detection in various elds [14][15][16]. With the rise of digital imaging technologies and advances in ML and DL techniques, automated glaucoma detection has gained signi cant traction in recent years.…”

Section: Introductionmentioning

confidence: 99%

Automated Glaucoma Detection Techniques: an Article Review

Abdulsalam,

Ali,

Hussien

2024

Preprint

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Glaucoma, a vision-threatening disease, poses a serious concern and requires early detection and intervention to avoid irreversible vision loss. Significant advances in automated glaucoma detection techniques have done through the using of machine and deep learning techniques. An overview of these techniques will be provided in the article review. What sets our review apart is its exclusive focus on ML and DL techniques for glaucoma detection using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines for filtering the papers. To achieve this, an advanced search on the Scopus database was conducted, specifically looking at research papers from 2023 with the keywords " Glaucoma AND detection OR machine AND learning OR deep AND learning ". Of the 324 papers found, we carefully selected 21 papers to include in our final review using the advanced query. The review offering valuable insights for clinicians and summarizes the recent techniques used by ML and DL for glaucoma detection, including the algorithms, databases, and evaluation criteria.

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Section: Introductionmentioning

confidence: 99%

Automated Glaucoma Detection Techniques: an Article Review

Abdulsalam,

Ali,

Hussien

2024

Preprint

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“…Highly dependable ubiquitous applications spanning multiple threat management, such as self-healing landmines, have recently piqued researchers' interests alongside cutting-edge applications. [5], [6]. In a WSN, sensor nodes (routers) communicate efficiently with one another by following a protocol that specifies the path from a source node to a destination node.…”

Section: Introductionmentioning

confidence: 99%

New Technology for Advanced Results for Security Wireless Sensor Networks

Al-Khafaji

2024

wjps

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Designing wireless sensor networks (WSNs), which are new technologies with the potential to perceive, analyze, communicate, and send data, is difficult since it impacts the network's performance and longevity. The goals of this study are to enhance network longevity, mitigate power usage, and safeguard transmitted data. An energy-saving procedure was suggested as a means to accomplish this objective. Every node in the network uses the same amount of electricity according to this protocol. The restricted resources of the nodes are also taken into account while recommending an algorithm. Additionally, in order to provide optimal outcomes for assessing the system's effectiveness. The suggested model and the network's performance were evaluated using the Quality of Service (Q o S) metrics of Packet Loss Ratio (PLR), Network Energy Consumption (NEC), and Network Throughput Ratio (NTR). , this method ensures that the data remains intact. Additionally, we came up with a method to save electricity. This protocol ensures that all nodes in the network use an equal amount of electricity

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“…The goal of ML, a branch of Artificial Intelligence (AI), is to develop systems that can learn from experience and advance without programming as in [3][4][5]. It can be classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Prediction using Machine Learning Methods: An Article Review

Hussein¹,

Abdulsalam²,

Shukur³

2023

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The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our systematic literature review demonstrates that ML-powered tools can alleviate the burden on healthcare systems. These tools can analyze significant amounts of medical data and potentially improve predictive and preventive healthcare.

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Emotion Recognition System Based on Hybrid Techniques

Cited by 19 publications

References 17 publications

Automated Glaucoma Detection Techniques: an Article Review

Automated Glaucoma Detection Techniques: an Article Review

New Technology for Advanced Results for Security Wireless Sensor Networks

Covid-19 Prediction using Machine Learning Methods: An Article Review

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