Artificial Intelligence in Predicting the Spread of Coronavirus to Ensure Healthy Living for All Age Groups

Stitini, Oumaima; Kaloun, Soulaimane; Bencharef, Omar

doi:10.1007/978-3-030-53440-0_2

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Relying solely on artificial intelligence methods may result in basic classification and user recommendations [20,21]. However, incorporating contextual data from IoT, along with the application of explainable artificial intelligence, particularly in Feature Selection and Engineering, has the potential to enhance the outcomes of the classification process.…”

Section: Motivationmentioning

confidence: 99%

Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare

Stitini,

Ouakasse,

Rakrak

et al. 2024

Int. J. Onl. Eng.

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The widespread adoption of the Internet of Things has significantly enhanced our daily lives across various dimensions. E-health has significantly benefited from advancements in the Internet of Things (IoT), particularly with the emergence of the Internet of Medical Things (IoMT). A sophisticated wireless sensor network produces a huge amount of data, requiring robust cloud-based hardware for precise processing and categorization. The IoMT allows for the extensive gathering of medical data from incoming hospital patients, enabling real-time monitoring of vital signs and health statuses. Nevertheless, effectively prioritizing patients in emergencies is challenging due to the importance and complicatedness of the data. To tackle this issue, an innovative solution involves integrating Explainable Artificial Intelligence into the IoMT ecosystem. By incorporating Explainable AI, the system enhances explainability, fostering trust and reliability in patient prioritization. This provides healthcare providers a more reliable prioritization mechanism that aligns with established medical guidelines. The study explores IoMT devices for collecting medical data from incoming patients, focusing on the MQTT protocol for lightweight devices, aiming to guide patients to the right department and prioritize emergency management through IoMT data analysis.

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Section: Motivationmentioning

confidence: 99%

Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare

Stitini,

Ouakasse,

Rakrak

et al. 2024

Int. J. Onl. Eng.

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“…The major goal of SA is to define the author's feelings as negative, positive, or neutral regarding different subjects [5]. To evaluate the effects of social media information relevant to the COVID-19 pandemic, research associated with people's opinions on medical information and applications gained major significance [6]. In particular, text analysis of Twitter information has been the emphasis in several reviews, allowing researchers to analyze massive instances of user-defined content to find views, which can inform decision-making and earlier reaction mechanisms [7].…”

Section: Introductionmentioning

confidence: 99%

Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

Almasoud,

Alshahrani,

Hassan

et al. 2023

Electronics

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In recent times, global cities have been transforming from traditional cities to sustainable smart cities. In text sentiment analysis (SA), many people face critical issues namely urban traffic management, urban living quality, urban information security, urban energy usage, urban safety, etc. Artificial intelligence (AI)-based applications play important roles in dealing with these crucial challenges in text SA. In such scenarios, the classification of COVID-19-related tweets for text SA includes using natural language processing (NLP) and machine learning methodologies to classify tweet datasets based on their content. This assists in disseminating relevant information, understanding public sentiment, and promoting sustainable practices in urban areas during this pandemic. This article introduces a modified aquila optimizer with a stacked deep learning-based COVID-19 tweet Classification (MAOSDL-TC) technique for text SA. The presented MAOSDL-TC technique incorporates FastText, an effective and powerful text representation approach used for the generation of word embeddings. Furthermore, the MAOSDL-TC technique utilizes an attention-based stacked bidirectional long short-term memory (ASBiLSTM) model for the classification of sentiments that exist in tweets. To improve the detection results of the ASBiLSTM model, the MAO algorithm is applied for the hyperparameter tuning process. The presented MAOSDL-TC technique is validated on the benchmark tweets dataset. The experimental outcomes implied the promising results of the MAOSDL-TC technique compared to recent models in terms of different measures. This MAOSDL-TC technique improves accuracy and interpretability of sentiment prediction.

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“…A recommender system is a processing method that measures the likelihood of a particular item being chosen by a specific person. The recommendation approaches are divided into eight categories as mentioned on Figure 1 based on how the recommendation is generated [6]: a collaborative filtering system, a content-based filtering system, a hybrid filtering system, a demographic recommender system, a knowledge-based recommender system, a risk-aware recommender system, a social network recommender system, and a context-aware recommender system. A content-based recommender system creates a user profile by evaluating the characteristics of specific items to predict and produce recommendations [7].…”

Section: Introductionmentioning

confidence: 99%

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

2022

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Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

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Artificial Intelligence in Predicting the Spread of Coronavirus to Ensure Healthy Living for All Age Groups

Cited by 6 publications

References 11 publications

Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare

Combining IoMT and XAI for Enhanced Triage Optimization: An MQTT Broker Approach with Contextual Recommendations for Improved Patient Priority Management in Healthcare

Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

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