Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

A Survey on Various Techniques in Internet of Things (IoT) Implementation: A Comparative Study

As per the current trends in computing research socialization and Personalization in Internet of Things (IOT) environment are quite trending and they are being widely used. The main aim of research work is to provide socialized and personalized services along with creating awareness of predicting the service. Here various kind of methods are discussed which can be used for predicting user intention in large variety of IOT based applications such as smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. By common consent it is found that the prediction is made usually for finding techniques that can be accessed by the mobile user, predicting the next page that is most likely to be used by web user, predicting favorite and most likely TV program that can be viewed by user, getting a list of browsing usage and need of user and also predicting user navigational patterns, predicting future climate conditions, predicting the health and welfare of user, predicting user intention so that implicit could be made and human-like interactions could be possible by accepting implicit commands, predicting the exact amount of traffic at a particular location, predicting curricular performance of student in schools & colleges, having prediction of frequency of natural calamities and their occurrences such as floods, earthquakes over a long period of time & also the required time in which precautionary measures could be adopted, predicting & detecting the frauds in which false user try to make transaction in the name of genuine user, predicting the steps and work done by the user to improve the business, predicting & detecting the intruder acting in the network, by the help of context history predicting the mood transition information of the user, etc. Here in this topic of discussion, different techniques such as Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms are used for prediction

Predicting user behavior using data profiling and hidden Markov model

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression.Comment: KDD 2023 accepte

Self-organization on social media: endo-exo bursts and baseline fluctuations

A salient dynamic property of social media is bursting behavior. In this paper, we study bursting behavior in terms of the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter. We found that there is a fluctuation threshold up to which the burst size increases as the fluctuation increases and that above the threshold, there appears a variety of burst sizes. We call this threshold the critical threshold. Investigating this threshold in relation to endogenous bursts and exogenous bursts based on peak ratio and burst size reveals that the bursts below this threshold are endogenously caused and above this threshold, exogenous bursts emerge. Analysis of the 3,000 keywords shows that all the nouns have both endogenous and exogenous origins of bursts and that each keyword has a critical threshold in the baseline fluctuation value to distinguish between the two. Having a threshold for an input value for activating the system implies that Twitter is an excitable medium. These findings are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, to predict the response to particular information on social media.Comment: Presented at WebAL-1: Workshop on Artificial Life and the Web 2014 (arXiv:1406.2507