"A Framework for Voting Behavior Prediction Using Spatial  Data "

Ravi, Shobhit Kumar; Chaturvedi, Shivam; Rastogi, Neeta; Akhtar, Nikhat; Perwej, Yusuf

doi:10.55524/ijircst.2022.10.2.4

Cited by 9 publications

(3 citation statements)

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“…The advancement of machine learning technology in recent years has opened up new opportunities for the resolution of challenging issues. Modern machine learning techniques like convolutional neural networks (CNNs) [38] and recurrent neural networks (RNNs) [39] have proven their effectiveness in applications including natural language processing and image categorization. By speeding up the related computational processes, the development of hardware technologies such as GPU computing has also accelerated the evolution of machine learning technologies.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner

et al. 2023

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Reinforcement learning is an area of Machine Learning. The three primary types of machine learning are supervised learning, unsupervised learning, and reinforcement learning (RL). Pre-training a model on a labeled dataset is known as supervised learning. The model is trained on unlabeled data in unsupervised learning, on the other hand. Instead of being driven by labels, RL is motivated by assessing feedback. By interacting with the environment and choosing the best course of action in each circumstance in order to maximize the reward, the agent learns the best way to solve sequential decision-making issues. The RL agent chooses how to carry out tasks on its own. Furthermore, since there are no training data, the agent learns by gaining experience. In order to make subsequent judgments, RL aids agents in efficiently interacting with their surroundings. In this essay, the state-of-the-art RL is thoroughly reviewed in the literature. Applications for reinforcement learning (RL) may be found in a wide range of industries, including smart grids, robots, computer vision, healthcare, gaming, transportation, finance, and engineering.

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Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner

et al. 2023

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“…Similar to this, the effectiveness of a collection of approaches is verified. To detect false profiles in online social networks, an ML [29] and NLP system is described in [30].…”

Section: Related Workmentioning

confidence: 99%

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

et al. 2023

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Currently, almost everyone spends more time on online social media platforms engaging with and exchanging information with people from all over the world, from children to adults. Our lives are greatly influenced by social media sites like Twitter, Facebook, Instagram, and LinkedIn. The social network is evolving into a well-liked platform for connecting with individuals across the globe. Social media platforms exist as a result of the enormous connectivity and information sharing that the internet has made possible. Social media's rising popularity has had both beneficial and detrimental consequences on society. However, it also has to deal with the issue of bogus profiles. False profiles are often constructed by humans, bots, or cyborgs and are used for phishing, propagating rumors, data breaches, and identity theft. Thus, we are emphasizing in this post the significance of setting up a system that can identify false profiles on social media networks. To illustrate the suggested concept of machine learning-based false news identification, we used the Twitter dataset for phony profile detection. The suggested model involves pre-processing to improve the dataset's quality and minimize its dimensions by modifying its contents and features. To forecast the bogus profiles, the widely used machine learning algorithms are used.

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“…This means that it randomly selects the feature split and child node values to guarantee enough variation among decision trees.Alternatively, in a Random Forest, we choose the feature splitting value using greedy algorithmic searching. Extra Trees and Random Forest[39] are quite similar with these two exceptions.4.6 K-Nearest Neighbour (KNN)When it comes to classification and regression, many machine learning experts turn to the K-Nearest Neighbors (KNN) method. The underlying premise is that labels or values for comparable data points are likely to be consistent.…”

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confidence: 99%

Machine Learning for the Diagnosis and Prognosis of Chronic Illnesses

Kajal,

Kanchan Saini,

Dr. Nikhat Akhtar

et al. 2024

Int J Sci Res Sci Eng Technol

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An essential part of healthcare is disease prediction, which seeks to identify people who are at risk of getting certain diseases. Because of their superior capacity to sift through massive datasets in search of intricate patterns, machine learning algorithms have recently become useful instruments in the fight against illness prediction. The goal of this project is to make it easier for people to diagnose their own health problems using just their symptoms and precise vital signs. Due to excessive medical expenditures, many people put off taking care of their health, which can result in worsening symptoms or even death. Medical expenses can be overwhelming for people without health insurance. Using machine learning methods like ExtRa Trees, the suggested system provides a general illness forecast based on patients' symptoms. The algorithm provides a possible diagnosis based on the user's age, gender, and symptoms, suggesting that the user may be experiencing a certain illness. The system also suggests healthy eating and exercise routines to help lessen the impact of the condition, depending on how bad it is. Lastly, this article has shown a comparison examination of the suggested system using several algorithms including logistic regression, decision tree, and Naïve Bayes. The efficiency and accuracy of illness prediction are both enhanced by the suggested model.

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"A Framework for Voting Behavior Prediction Using Spatial Data "

Cited by 9 publications

References 0 publications

A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner

A Review of Current Perspective and Propensity in Reinforcement Learning (RL) in an Orderly Manner

Novel Approaches to Detect Phony Profile on Online Social Networks (OSNs) Using Machine Learning

Machine Learning for the Diagnosis and Prognosis of Chronic Illnesses

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