International Journal of Next-Generation Computing

Deep ensemble learning for intelligent healthcare computing: A case study of Alzheimer’s disease

Fri, 09 Aug 2024 00:00:00 +0530

The growing popularity of deep learning (DL) in recent years has encouraged researchers to diversify their applications further. The limitations and shortcomings of an individual model are subdued through ensemble learning (EL), which combines the predictions of multiple models that are trained separately, thereby improving the overall accuracy and robustness. Deep ensemble learning (DEL) models leverage the combined diversity of different deep learning models. This paper provides an overview of traditional, novel, and state-of-the-art deep ensemble methods for application in Alzheimer's disease (AD) and other intelligent healthcare applications, including bagging, boosting, stacking, homogeneous/heterogeneous ensembles, explicit/implicit ensembles, negative correlation-based deep ensemble models and decision fusion. For this research study, an extensive exploration was conducted across prominent academic databases, including Google Scholar, ProQuest, DBLP, Science Direct, MDPI, IEEE Xplore, and Springer. The investigation encompassed a meticulous search for literature between 2018 and 2023 to ascertain the study's most current and relevant data. The results are presented through various methodologies, including flow charts, graphs, figures, and comparative tables, ensuring a comprehensive and visually accessible representation of the findings. This survey paper presents performance results from diverse ensemble methods applied to deep learning models. This reveals significant performance enhancements on specific datasets and model combinations, showcasing the impactful role of ensembling in surpassing individual model outcomes. Our findings also highlight nuanced correlations between ensemble techniques and data characteristics, offering actionable insights for implementing optimized ensemble-based deep learning models in clinical settings. This novel contribution underscores our paper's advancement in Alzheimer's detection methodologies, uniting comprehensive data analysis, ensemble effectiveness, and valuable considerations.

Enhancing User Authentication via Deep Learning: A Keystroke Dynamics Approach

Kartik N Iyer, Harsh K Upadhyay, Ravirajsinh S Vaghela — Fri, 09 Aug 2024 00:00:00 +0530

User authentication is an important process that ensures only authorized individuals can access a system or network infrastructure. This process protects users’ sensitive information from unauthorized access and prevents any unwanted tampering. In this research paper, a unique method for authenticating users based on typing dynamics has been proposed, aimed to enhance the security of systems and networks by verifying user identities. This method involves calculating the period of time each key is pressed and released. The study includes data collection, feature extraction, model training, and performance evaluation by measuring the accuracy and precision of the trained model. We evaluated three deep-learning models to test the proposed method and determine its accuracy, precision, and superiority among the three models. Based on the findings of this research, we are able to present an algorithm which outperforms the other two considered for the experiment. Also, a comparative study is been presented after the first evaluation which involves assessing the accuracies for different lengths of the password. Additionally, charts and graphs were carefully employed to ensure precise representation and effective visualization of the data.

Deep Learning based Automated System for Banana Plant Disease Detection and Classification

Manojkumar Patel, Pradip Patel — Fri, 09 Aug 2024 00:00:00 +0530

In India, one of the primary agricultural practices is the production of bananas. A prevalent issue in farming is that the crop has been impacted by multiple illnesses. Disease identification in bananas has been shown to be more difficult in the field because the fruit is prone to various diseases and causes farmers to suffer significant losses. Consequently, this study aimed at developing an automatic system for the early detection and classification of banana plant diseases using deep learning. Three pre-trained convolutional neural network models MobileNet, VGG16, and InceptionV3 are used to classify banana disease images. The banana disease images dataset from the PSFD-Musa Dataset is utilized for training, validation, and testing. The proposed system is developed and checked to classify banana plant disease photographs into one of seven categories. The MobileNet achieved an accuracy of 96.72%, VGG16 an accuracy of 55.68%, and InceptionV3 an accuracy of 63.65%.

A Clustering Based Approach for Topic Categorization using GloVe Technique

Farha Naznin, Irani Hazarika, Anjana Kakoti Mahanta — Fri, 09 Aug 2024 00:00:00 +0530

Topic extraction and categorization is an important task because by doing that it is easy to find out which are the topics most discussed by the users in their tweets or opinions and need to be analyzed. In this work, topics are extracted from positive and negative opinions and then categorized into different groups. For performing this, first a collection of opinions is divided into two sets- positive opinions and negative opinions by using a sentiment analyzer. Then a method is proposed to find out the most discussed topics in the set of positive opinions and negative opinions. For extracting the topics from a set of opinions the noun words are extracted from the set of the opinions. After extracting the topics, the similar topics have been combined by using synonymy relation. Then the frequent topic words are represented with the help of GloVe embedding technique. Finally, the topics are categorized by using a clustering algorithm by applying it on the frequent topic words. For the evaluation of the proposed method, tweets from a Twitter User dataset are used. The results obtained from the experiments by applying the proposed method on the dataset give promising result and provide interesting and meaningful clusters of topics. Moreover, an analysis of the result obtained for both positive and negative opinions is also presented.

A Comparative Analysis of Machine Learning Techniques for Efficient Diabetes Prediction

Tajinder Kaur, Sikander Singh Cheema, Lakhwinder Kaur — Fri, 09 Aug 2024 00:00:00 +0530

In the healthcare sector, predictive analytics plays a vital role, presenting a challenging task but offering potential benefits in making informed decisions about patient health and treatment based on big data. This research paper delves into the realm of predictive analytics in healthcare, employing four distinct machine learning algorithms. The experiment involves the utilization of a dataset comprising patients’ medical records, upon which the four algorithms are applied. A comprehensive analysis is conducted using a diverse range of algorithms, including logistic regression, decision trees, random forests and support vector machines. These algorithms’ effectiveness is assessed using important measures like precision, recall, precision, accuracy and F1-score. By comparing the different machine learning techniques employed in the present study, the analysis aims to determine the most suitable algorithm for predicting diabetes.

Standardized Electronic Health Record and its Controlled Access

Mamta Dhaka, Durga Prasad Sharma, Priyansh Sharma — Fri, 09 Aug 2024 00:00:00 +0530

The Electronic Health Record (EHR) is a digitalized solution to support the health care facility, irrespective of levels and sizes to improve patient care system by eliminating the paper based medical records.Standardization of EHR improves the easy sharing of health information between various levels of health care system. The availability of the patient’s data in a timely fashion can contribute to the improvement of patient’s information and performance of the Health Information System. Current health care information systems of the hospitals are usually isolated from each other as most of the hospitals and health care institutions have their own format to create EMR (Electronic Medical Records) to serve the purpose of treating the patient.Standard coding makes it simple to share health information, lowers uncertainty, enhances workflow, and makes it easier to accurately analyze data related to health care.During patient registration or hospital visit, ID proof like Aadhar Number isused as a universal patient identifier. Healthcare user authentication is archived at database level through valid user name and password.The cloud server checks the credentials against a user store of the database for validation as illustrated in Algorithm-1.The primary function of the attribute based access control (ABAC) provided by Algorithm-2 is to authorize access for healthcare users. The hospital authorities obtain the patient's agreement in the first stage, and the loop is continued by using the value YES.The role based access control (RBAC) given in Tables-II and III is one of the best method for highly complex and huge management system. All this process standerdize EHR and its controlled access safe and secure.