What is Artificial Intelligence?

Morandín-Ahuerma, Fabio

doi:10.55248/gengpi.2022.31261

Cited by 23 publications

(13 citation statements)

References 10 publications

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“…Additionally, the Improved Bi-GRU+LSTM gained minimal FNR ratings than the conventional schemes in all the training rates. Mainly, the FPR of the Improved Bi-GRU+LSTM methodology is 0.086 (Training Percentage=90), this is incredibly lower than Dtree [21], Bi-LSTM [22], CNN, SVM, DNN, RF and RNN. Altogether, the exceptional achievement in the negative metric reaffirms the capability of the Improved Bi-GRU+LSTM scheme to precisely forecast the disease by leveraging improved fuzzy entropy and hybrid classification scheme involving LSTM and Bi-GRU.…”

Section: Validation On Negative Metricmentioning

confidence: 92%

“…In 2021, Anirban Chakraborty, [21] has framed a novel health monitoring by employing AI, in which the objective of this system is more significant and offered efficient requirement and resources. Moreover, this proposed structure was implemented by employing flask infrastructure and predicted the heath status of patient efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Various evaluation metrics including specificity, NPV, accuracy, MCC, FPR, F-measure, precision, FNR, and sensitivity were employed. Furthermore, the study conducted comparisons with state-of-the-art approaches such as Dtree [21] and Bi-LSTM [22]. Additionally, Improved Bi-GRU+LSTM was compared against traditional classifiers like CNN, SVM, DNN, RF and RNN.…”

Section: Performance Analysismentioning

confidence: 99%

“…The evaluation on positive metric of the Improved Bi-GRU+LSTM over Dtree [21], Bi-LSTM [22], CNN, SVM, DNN, RF and RNN for disease prediction system is indicated in fig 4 . To achieve precise disease prediction, the model must reach higher positive metric values. Similarly, the Improved Bi-GRU+LSTM generated superior outcomes with accurate prediction of disease.…”

Section: Validation On Positive Metricmentioning

confidence: 99%

“…Here, as the learning percentage increases the negative metric value gradually decreased for the Improved Bi-GRU+LSTM methodology. More particularly, at the training rate 90, the FPR of the Improved Bi-GRU+LSTM approach is 0.081, even though the Dtree [21] (0.156), Bi-LSTM [22] (0.118), CNN (0.147), SVM (0.158), DNN (0.162), RF (0.147) and RNN (0.158), correspondingly. Additionally, the Improved Bi-GRU+LSTM gained minimal FNR ratings than the conventional schemes in all the training rates.…”

Section: Validation On Negative Metricmentioning

confidence: 99%

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Healthcare monitoring and Recommendation model: Hybrid Deep learning Enabled Disease Diagnosis Framework

Mallapur

2024

Preprint

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The continuous observation of patients’ health condition as well as monitoring varying crucial symptoms is termed as Healthcare monitoring, which also detect and trace any probable problems. This kind of observation is more crucial for detecting health conditions at early stage especially, recognizing cardiovascular disease in diabetes patient. However, diagnosing cardiovascular disease in diabetes patient is extremely difficult because of the small quantity of labeled data with the prevalence of outliers in the diabetes datasets. This paper proposes a novel healthcare monitoring and recommendation model with hybrid DL based disease diagnosis structure considering the heart disease. The improved z-score normalization is employed to preprocess the input data by scaling the numeric features within a general range. The BWSV-FE based feature extraction process is proposed to extract the entropy features; together with this feature, a raw feature and mutual information features are collected. Subsequently, the hybrid disease prediction model is proposed that includes improved Bi-GRU and LSTM models. Improved Bi-GRU is adopted for efficiently predicting the disease, in which the CCE is updated with the modified MSE loss function by inducing penalty function into this. Moreover, the penalty is computed by employing Gini Index function. Thereby, the hybrid disease prediction model efficiently predicts the diseases’ severity level that aids the doctor for guiding the management of the patients’ health.

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Section: Validation On Negative Metricmentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Section: Validation On Positive Metricmentioning

confidence: 99%

Section: Validation On Negative Metricmentioning

confidence: 99%

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Healthcare monitoring and Recommendation model: Hybrid Deep learning Enabled Disease Diagnosis Framework

Mallapur

2024

Preprint

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Balancing Efficiency and Ethics: The Challenges of Artificial Intelligence Implementation in Journalism

Aljalabneh,

Aljawawdeh,

Mahmoud

et al. 2024

Studies in Systems, Decision and Control

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Strategic view on the current role of AI in advancing environmental sustainability: a SWOT analysis

Greif,

Kimmig,

El Bobbou

et al. 2024

Discov Artif Intell

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Sustainability has become a critical global concern, focusing on key environmental goals such as achieving net-zero emissions by 2050, reducing waste, and increasing the use of recycled materials in products. These efforts often involve companies striving to minimize their carbon footprints and enhance resource efficiency. Artificial intelligence (AI) has demonstrated significant potential in tackling these sustainability challenges. This study aims to evaluate the various aspects that must be considered when deploying AI for sustainability solutions. Employing a SWOT analysis methodology, we assessed the strengths, weaknesses, opportunities, and threats of 70 research articles associated with AI in this context. The study offers two main contributions. Firstly, it presents a detailed SWOT analysis highlighting recent advancements in AI and its role in promoting sustainability. Key findings include the importance of data availability and quality as critical enablers for AI’s effectiveness in sustainable applications, and the necessity of AI explainability to mitigate risks, particularly for smaller companies facing financial constraints in adopting AI. Secondly, the study identifies future research areas, emphasizing the need for appropriate regulations and the evaluation of general-purpose models, such as the latest large language models, in sustainability initiatives. This research contributes to the growing body of knowledge on AI’s role in sustainability by providing insights and recommendations for researchers, practitioners, and policymakers, thus paving the way for further exploration at the intersection of AI and sustainable development.

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What is Artificial Intelligence?

Cited by 23 publications

References 10 publications

Healthcare monitoring and Recommendation model: Hybrid Deep learning Enabled Disease Diagnosis Framework

Healthcare monitoring and Recommendation model: Hybrid Deep learning Enabled Disease Diagnosis Framework

Balancing Efficiency and Ethics: The Challenges of Artificial Intelligence Implementation in Journalism

Strategic view on the current role of AI in advancing environmental sustainability: a SWOT analysis

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