Detecting Information on the Spread of Dengue on Twitter Using Articial Neural Networks

Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques.Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media.The amount of data generated by social media platforms,such as Twitter,that can be used to track diseases is increasing rapidly.This paper proposes a method for the classication of tweets related to the outbreak of dengue using machine learning algorithms.An articial neural network(ANN)-based method is developed using Global Vector(GloVe)embedding to use the data in tweets for the automatic and efcient identication and classication of dengue.The proposed method classies tweets related to the outbreak of dengue into positives and negatives.Experiments were conducted to assess the proposed ANN model based on performance evaluation matrices(confusion matrices).The results show that the GloVe vectors can efciently capture a sufcient amount of information for the classier to accurately identify and classify tweets as relevant or irrelevant to dengue outbreaks.The proposed method can help healthcare professionals and researchers track and analyze epidemic outbreaks through social media in real time.

Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

Diabetes mellitus,generally known as diabetes,is one of the most common diseases worldwide.It is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin injections.Diabetes is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/dL.Diabetes is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood cells.Diagnosis and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of error.The primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing diabetes.Historically,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of diabetes.However,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare professionals.Therefore,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

Correction of sensor’s dynamic error caused by system limitations

The method based on particle swarm optimization(PSO)integrated with functional link articial neural network(FLANN)for correcting dynamic characteristics of sensor is used to reduce sensor’s dynamic error caused by its system limitations.Combining the advantages of PSO and FLANN,with this method a dynamic compensator can be realized without knowing the dynamic model of the sensor.According to the input and output of the sensor and the reference model,the weights of the network trained were used to initialize one particle station of the whole particle swarm when the training of the FLANN had been finished.Then PSO algorithm was applied,and the global best particle station of the particle swarm was the parameters of the compensator.The feasibility of dynamic compensation method is tested.Simulation results from simulator of sensor show that the results after being compensated have given a good description to input signals.