Supported matrix factorization using distributed representations for personalised recommendations on twitter

Kumar, Akshi; Ahuja, Himanshu; Singh, Nikhil Kumar; Gupta, Deepak; Khanna, Ashish; Rodrigues, Joel J. P. C.

doi:10.1016/j.compeleceng.2018.08.007

Cited by 13 publications

(2 citation statements)

References 2 publications

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“…The most recent numerous years, ocean level ascent made by the effects and environmental difference in its on shoreline zones has spun into an issue of developing interest with the therapeutic culture, furthermore to overall population and the press. It is presently more made that the Earth's atmosphere is warming that the primary driver will be the development of regular house gases (GHGs) in the Journal of Artificial Intelligence and Systems planet, created by anthropogenic non-renewable energy source burning just as change in land use (generally deforestation) [30]. Environmental change has offered ascend to a ton of unmistakable impacts, especially advancement of the Earth's forceful region temperature and of marine warming content, dissolving of marine ice and glaciers and damage of ice mass from the Greenland and Antarctica ice sheets [31].…”

Section: Sea Level Risementioning

confidence: 99%

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Malik¹,

Pande²,

Nishi³

et al. 2020

AIS

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Climate change issues societal operation, likely wanting considerable adaptation to deal with doing well altered weather patterns. Machine learning (ML) algorithms have progressed considerably, triggering breakthroughs in some other investigation sectors, along with only lately suggested as helping climate evaluation. Though a significant volume of isolated Earth System functions are analyzed with ML techniques, much more generic phone system to find out better the whole temperature unit hasn't happened. For instance, ML is able to aid remote identification, in which complex feedbacks make characterization tough from instantaneous equation analysis or perhaps possibly visualization of sizes plus Earth System design (ESM) diagnostics. Artificial intelligence (AI) may thus build on determined climate associates to provide enhanced alerts of approaching eco-friendly functions, which includes intense events. While ESM development is actually completely necessary, a parallel concentrate on utilizing ML and AI to determine as well as capitalize a great deal more on pre pre-existing simulations as well as info is suggested by us.

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Section: Sea Level Risementioning

confidence: 99%

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Malik¹,

Pande²,

Nishi³

et al. 2020

AIS

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“…Thus, these reviews are analyzed for negative opinion polarity using AFINN-111 sentiment lexicon, which is a list of 2477 English words labeled with sentiment strength [12]. Sentiment refers to the use of polarities (positive and/or negative) in written text [13]- [16]. Each word is assigned with an integer in a range of polarity from −5 up to +5, negative to positive.…”

Section: Related Workmentioning

confidence: 99%

A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

et al. 2019

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The term web-app defines the current dynamic pragmatics of the website, where the user has control. Finding faults in such dynamic content is challenging, as to whether the fault is exposed or not depends on its execution path. Moreover, the complexity and uniqueness of each web application make fault assessment an extremely laborious and expensive task. Also, artificial fault injection models are run in controlled and simulated environments, which may not be representative of the real-world fault data. Classifying faults can intelligently enhance the quality of the web-apps by the assessment of probable faults. In this paper, an empirical study is conducted to classify faults in bug reports of three open-source web-apps (qaManager, bitWeaver, and WebCalendar) and reviews of two play store web-apps (Dineout: Reserve a Table and Wynk Music). Five supervised learning algorithms (naïve Bayesian, decision tree, support vector machines, K-nearest neighbor, and multi-layer perceptron) have been first evaluated based on the conventional term frequency-inverse document frequency (tf-idf) feature extraction method, and subsequently, a feature selection method to improve classifier performance is proposed using particle swarm optimization (a nature-inspired, meta-heuristic algorithm). This paper is a preliminary exploratory study to build an automated tool, which can optimally categorize faults. The empirical analysis validates that the particle swarm optimization for feature selection in fault classification task outperforms the tf-idf filter-based classifiers with an average accuracy gain of about 11% and nearly 26% average feature reduction. The highest accuracy of 93.35% is shown by the decision tree after feature selection.

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Impact analysis of government microblog participation in public management based on association analysis and edge computing

Hu¹,

Yao

2022

Concurrency and Computation

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The government affairs microblogging platform helps the government to interact effectively with the public, thereby guiding the public to combine the demands of the real society with the expressions in the virtual community and achieve orderly public participation. This paper adopts a combination of association analysis and edge computing to analyze the impact of government microblog participation in public management. First, this paper designs a government microblog evaluation framework based on edge computing technology. Second, in order to deeply understand the relationship between highly active government microblogs and public trust, this paper evaluates the influence of government microblogs by correlating indicators such as forwarding volume and number of comments. The results show that the evaluation results obtained by this model have little difference with the expert evaluation results. The above scheme helps to deepen the theoretical study of media credibility in the context of social media.

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Supported matrix factorization using distributed representations for personalised recommendations on twitter

Cited by 13 publications

References 2 publications

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

Artificial Intelligence and Machine Learning to Assist Climate Change Monitoring

A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps

Impact analysis of government microblog participation in public management based on association analysis and edge computing

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