Big Data Analytics in Healthcare: Transforming Patient Care, Operational Efficiency, and Stroke Management

Palagati; Yanda

doi:https://doi.org/10.54216/JCHCI.080101

Fusion: Practice and Applications | 2024

Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language

This study explores the enhancement of accuracy in Indonesian sentiment analysis by incorporating text segmentation features during the pre-processing phase. One of the most important steps in creating a high-quality Bag of Words is to separate Indonesian sentences with no spacing, which is made possible by the created text segmentation algorithm. Through the conducted observations and analyses, it was observed that text comments from social media frequently exhibit connected sentences without spacing. The segmentation process was developed through a matching model utilizing a standard Indonesian word dictionary. Implementation involved testing Indonesian text data related to COVID-19 management, resulting in a substantial increase of 3,036 features. The Bag of Words was then constructed using the Term Frequency-Inverse Document Frequency method. Subsequently, sentiment analysis classification testing was conducted using both deep learning and machine learning models to assess data quality and accuracy. The sentiment analysis accuracy for applying Deep Learning, Support Vector Machine and Naive Bayes is 86.46%, 88.02% and 86.19% respectively.

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Siti Mujilahwati mail -

Noor Zuraidin M. Safar mail -

Catur Supriyanto mail

link https://doi.org/10.54216/FPA.150213

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Neural Network Feature Selection Based on Collaborative Filtering Recommender Systems for User Classification

In today’s competitive markets, it is crucial to render personalized assistance tailored to unique individual’s needs. To accomplish this goal, a recommender system represents a noteworthy progression in collaborative filtering recommender systems. This shift highlights a broader research focus that extends beyond algorithms to encompass a diverse array of questions related to the functionality of the recommender. The identification accuracy must be assessed as a function of how well the suggested approach fits with a user's wants and needs, particularly in the context of collaborative constraint-based functions. The next phase of research must focus on defining parameters for assessment which may be used to compare the performance of constraint-based algorithms across a wide variety of diverse issues. It is currently necessary to design, or at criteria for assessment for constraint-based algorithms. We have addressed key research challenges related to the following topics: constraint-aware machine learning, understanding parameters in solution spaces, metrics for assessing constraint-based systems, algorithm selection, machine learning considerations, and investigating constraint-based platforms, and elucidations. 

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Elham Abdulwahab Anaam mail -

Su-Cheng Haw mail -

Kok-Why Ng mail -

Palanichamy Naveen mail

link https://doi.org/10.54216/FPA.150214

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Multiple Polynomial Regression Model for Predicting Surface Roughness of Titanium Alloy in Electrical Discharge Machining

This study investigated the experimental work of titanium alloy in the die-sinking electrical discharge (EDM) machining process to enhance surface integrity (surface roughness) by applying regression-based modeling. Furthermore, a multiple polynomial regression (MPR) model was developed to predict surface roughness responses under optimized conditions. The effects of EDM parameters, such as pulse-on time (ON), pulse-off time (OFF), peak current (IP), and servo voltage (SV), on surface roughness were studied. The experiment was conducted using a two-level full factorial design with four center points. Roughness was measured using a surface roughness tester (Formtracer SJ-301). The significant cutting parameters for surface roughness were determined using analysis of variance (ANOVA). The results showed that increasing the servo voltage significantly reduced the surface roughness by 46.48%. The developed model also predicted surface roughness values lower than those observed in the experimental data, with an R2 value of 0.608.

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Nurezayana Zainal mail -

Azlan Mohd Zain mail -

Mohamad Firdaus A. Aziz mail -

Salama A. Mostafa mail -

Ashanira Mat Deris mail -

Nor B. Abd Warif mail -

Muhammad Ammar S. Shahrom mail

link https://doi.org/10.54216/FPA.150215

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Exploring Advanced Techniques in Multilevel Fusion Score Level for Enhanced Data Integration in Complex Systems

We have discovered five novel strategies to enhance data fusion in complex systems. This page provides a comprehensive explanation of these five methodologies. Data may be combined with a list. Examples of techniques include entropy-based data selection and parameter optimization for data fusion. This technique effectively resolves all problems related to merging records. Accurate, rapid, and easily expandable. Ablation studies assess the effectiveness of various techniques. Every process is crucial; omitting anyone would adversely affect the mix. This approach may integrate data from several sources to guarantee accuracy and utility. This facilitates the use of intricate technologies, hence enhancing data integration. The study promotes further inquiry and implementation. These results indicate that using this method might enhance the process of combining data.

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Tri Rijanto mail -

B. Santhosh Kumar mail -

Aws Zuhair Sameen mail -

Takveer Singh mail -

Suruchi Pimple mail -

Swati M. Patil mail

link https://doi.org/10.54216/FPA.150216

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

An efficient Analysis of the Fusion of Statistical-Centred Clustering and Machine Learning for WSN Energy Efficiency

Recently, wireless sensor networks on several challenging topics have piqued researchers’ attention. Maximising a network's lifetime requires just the right combination of cluster size and number of nodes. Data transmission from nodes to cluster leaders is energy intensive, even for a modest number of clusters. If there are several clusters, many leaders will be chosen, and many nodes will rely on long-distance transmission to communicate with the home base. Therefore, in order to maximise efficiency, it is necessary to strike a balance between these two factors. WSN's major challenge is improving its energy efficiency. This is because their energy consumption defines their lifespan, and it is difficult, if not impossible, to recharge their batteries. Therefore, it is crucial to develop algorithms that consume as little energy as possible in order to maximise the network's potential. The perfect clusters are essential for the longevity of the network. Therefore, an algorithm called statistical centre energy efficient clustering approach (SEECA) is presented to increase the network's lifetime while decreasing its energy consumption. The experimental findings show that the proposed methodology SCEECA outperforms the LEACH method by a wide margin, with gains of 32% in Residual energy, 16% in Network Lifetime, and 12% in Throughput.

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Deepak Dasaratha Rao mail -

Bala Dhandayuthapani V. mail -

Ch. Subbalakshmi mail -

Murlidhar Prasad Singh mail -

Prashant Kumar Shukla mail -

Shraddha V. Pandit mail

link https://doi.org/10.54216/FPA.150217

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Hybrid CNN-XGB Framework for Enhancing Human Activity Recognition

Human Activity Recognition (HAR) is one of the most important modern research fields concerned with studying and analyzing human actions and behaviors. Human activity recognition applications offer great potential for a wide range of applications in various fields that enhance health, safety, and efficiency. Due to the diversity of human activities and the way people carry out these activities, it is difficult to recognize human activity. The amazing capabilities provided By Artificial Intelligence (AI) tools in analyzing and understanding hidden patterns in complex data can greatly facilitate the HAR process. There has been a huge trend in the past 10 years to use Machine Learning (ML) and Deep Learning (DL) techniques to analyze and understand big data for HAR. Although there are many studies using these techniques, their accuracy still needs to be further improved due to several challenges: Data complexity, class imbalance, determining the appropriate feature selection technique with ML technique, and tuning the hyperparameters of the used ML technique. To overcome these challenges, this study proposes an effective framework based on two stages: a data preprocessing procedure that includes data balance and data normalization. Then, a hybrid CNN-XGB model combining Convolutional Neural Network (CNN) and a fine-tuned XGBoost (XGB) classifier is developed for accurate HAR. The CNN-XGB model achieved excellent results in HAR when trained and tested on the HCI-HAR dataset, achieving an accuracy of up to 99.0%. Effectively HAR provides the opportunity to apply many applications that contribute to improving the quality of life in various areas of our daily lives.

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Farah Hatem Khorsheed mail -

Raniah Hazim mail -

Sarah. A. hassan mail -

Qusay Saihood mail

link https://doi.org/10.54216/FPA.150218

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed

The research article "Harnessing the Power of Machine Learning to Refine Data Fusion Processes for Better Accuracy and Speed" proposes integrating different machine learning methods to improve data fusion. The suggested method uses an ensemble learning strategy, a deep learning-based fusion model, SVMs for data combining, CNNs for image and time-series data combining, and RNNs for time-series data combining. For best efficiency, each algorithm is carefully constructed utilizing mathematical concepts. Deep learning shines on complicated datasets, whereas the ensemble approach, which uses several models, is more accurate. CNN handles visual data better than RNN does sequence data. However, SVM shines in multidimensional domains. These reliable and adaptive solutions can tackle various data fusion difficulties. This approach outperforms others in processing speed, accuracy, precision, memory, and F1-score. Finding a balance between computer complexity and human satisfaction enhances dependability, data duplication, and quality. This novel technique transforms machine learning-powered data fusion. Another benefit is better data integration in complicated systems.

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Yuliadi Erdani mail -

Ankit Kumar Dubey mail -

Aws Zuhair Sameen mail -

Saksham Sood mail -

Ramanchi Radhika mail -

Mohammad Ahmar Khan mail

link https://doi.org/10.54216/FPA.150219

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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International Journal of Wireless and Ad Hoc Communication | 2024

The Smart Trust framework for WBAN: An AI-driven approach for node trust assessment

The primary contribution of this research lies in its innovative use of artificial intelligence to automate the trust assessment process in WBANs, providing a dynamic solution to the challenge of maintaining data integrity and network reliability. The SmartTrust (SmTr) framework uses advanced machine learning techniques to accurately analyze historical and behavioral data of network nodes. Thus, computer trustworthiness scores allow one to effectively distinguish between trustworthy nodes and potentially malicious nodes. WBANs and their services are rapidly gaining popularity, but they pose unprecedented security challenges. These requirements are being met with WBAN as it evolves. In an increasingly complex, heterogeneous, and evolving mobile environment, completing these tasks can be difficult. A more secure and adaptable WBAN environment can be achieved by using trust management to meet WBAN security requirements. The reliability of a wireless sensor network is evaluated through behavioral evidence. Researchers use the results of node behavior almost directly or combine them with the results of third-party evaluation, instead of studying the original evidence of node behavior and ignoring the analysis of the history of node behavior, which leads to low confidence, rationality, and reliability. SmartTrust (SmTr) is a new approach based on artificial intelligence (AI) to improve trust and reliability over wireless body area networks (WBAN). As a modern healthcare system, this technology can be considered. Experimental results from implementing the SmTr framework demonstrate its effectiveness in improving network resilience against security threats, improving resource allocation, and thus increasing the quality and reliability of healthcare delivery.

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Hala Shaker Mehdy mail

link https://doi.org/10.54216/IJWAC.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2024

On Neutrosophic Crisp Grill Topological Spaces

Among the important and ancient mathematical concepts are the concepts of grill, the local function, and the knowledge of using grill in conjunction with topological spaces, which have gained wide scope in the natural sciences and elsewhere. The main idea here is to development these concepts in conjunction with neutrosophic crisp sets, and on the other hand to highlight their important and influential properties and the relationships that link them to each other.

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Elaf Ali Sfook mail -

L. A. A. Al-Swidi mail

link https://doi.org/10.54216/IJNS.240126

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2024

Big Data Analytics in Healthcare: Transforming Patient Care, Operational Efficiency, and Stroke Management

A significant amount of sensor data and patient health data files are being created in the current era of smart phones and wearable technology. Big data analytics is crucial to resolving problems and obstacles in the healthcare industry. The healthcare industry generates enormous amounts of data that big data can handle. Every day, a variety of devices generate petabytes of data, which, when examined, can provide insightful and practical data-driven solutions for patient care. This paper provides an overview of the various healthcare applications of big data analytics, along with an analysis of the associated problems and potential tools and technologies for healthcare clouds. Big data has the power to transform the healthcare sector and enhance clinical trial monitoring quality and operational efficiency.

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Palagati Anusha mail -

Yanda Sailaja mail

link https://doi.org/10.54216/JCHCI.080101

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

Details open_in_new