Mobile crowd-sensing (MCS) is a new sensing paradigm that takes advantage of the extensive use of mobile phones that collect data efficiently and enable several significant applications. MCS paves the way to explore new monitoring applications in different fields such as social networks, lifestyle, healthcare, green applications, and intelligent transportation systems. Hence, MCS applications make use of sensing and wireless communication capabilities provided by billions of smart mobile devices, e.g., Android and iOS-based mobile devices. The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes. We discuss the main components of the infrastructure required to support the proposed framework. The existing and potential applications leveraging MCS are laid out. Furthermore, this paper discusses the current challenges facing the collection methodologies of the participants’ data in task management. The recent issues in the MCS findings are reviewed as well as the opportunities and challenges in sensing methods are analyzed. Finally, open research issues and future challenges facing MCS are highlighted.

/pdf/a-survey-on-mobile-crowd-sensing-and-its-applications-in-the-3sq151fs4p.pdf

A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era

Achieving efficient and privacy-preserving truth discovery in crowd sensing systems

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.

/pdf/an-empirical-study-on-machine-learning-algorithms-for-heart-2ir7s07t.pdf

An empirical study on machine learning algorithms for heart disease prediction

Online user feedback about software products is a promising source of user requirements. To allow scaling analyses to large amounts of user feedback, research on Crowd-based Requirements Engineering (CrowdRE) seeks to tailor natural language processing (NLP) techniques to Requirements Engineering (RE). Various frameworks have been proposed, but it remains largely unclear why particular NLP techniques are better suited for CrowdRE than others, which makes it hard to make a well-founded choice for a technique. We found that CrowdRE research most often uses machine learning (ML) and has so far applied twelve clusters of ML algorithms and seven clusters of ML features. The prevalent algorithm–feature pair is Näıve Bayes with Bag of Words – Term Frequency (BOW-TF), followed by Support Vector Machines (SVM) with BOW-TF. An initial comparison of the reported precision and recall suggests that classifications in RE need further improvement. Our research presents a preliminary overview of the current landscape of automated classification techniques for RE whose results may inspire researchers to apply new strategies to advance research in this field, or to include ML models they had not considered previously in their benchmarks.

/pdf/an-overview-of-user-feedback-classification-approaches-3idtsx87gb.pdf

An Overview of User Feedback Classification Approaches.

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets–datasets sourced from people with disabilities and older adults—that can potentially play an important role in mitigating bias for inclusive AI-infused applications. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.

pdf/data-representativeness-in-accessibility-datasets-a-meta-anf3i0gg.pdf

Data Representativeness in Accessibility Datasets: A Meta-Analysis

Modern technology generates such a huge amount of public and private datasets that its security becomes an inevitable task. Initially the priority was provided for data security for the data of organization's and firm's, but nowadays it is necessary to provide security for personal data as well. So to achieve the data security, it is mandatory to preserve the privacy of personal information for that we use anonymization technique such as generalization, bucketization, multi set-based generalization, one-attribute-per-column slicing, slicing and suppression with slicing are applied to avoid retrieval of data from database. Thus, privacy preservation means to protect the data value and it is used for data mining in order to get the valid and accurate results. These are discussed and successfully analyzed with different parameters such as revealed co-relation quality (linkage property), loss of information, type of data, security (privacy preserved) and membership disclosure in this paper. The analysis shows that suppression with slicing is an innovative technique that preserves the privacy of identity of an individual in a database better than previously mentioned techniques.

Analysis of data security by using anonymization techniques

A fist size muscle occupies an important in the human body by supplying oxygen to all the body organs. According to study of demography from WHO (World Health organization), the main cause of increasing death rate is due to the cardiac failure of human being. The main challenge for data analysis is to predict and prevent the heart disease. Machine learning has been developed to perform impressive predictions and make appropriate decision from abundant data originated by healthcare centres. In this paper numerous machine learning techniques are surveyed by using the knowledge collected from preprocessing data (clinical knowledge), which comprises many medical features to perform heart disease detection. The comparative study states that the prediction of heart disease has been improved by combining various machine learning algorithms to perform early disease investigation in a cost effective manner. The proposed research work primarily focuses on preparing a review of the research done by different professionals and compiling it into one paper and creating a direction for future research in this domain. In this paper many techniques are surveyed where best predictions are performed for heart disease.

A Study on Role of Machine Learning in Detectin Heart Diseas.

Now days, the power of internet is having an immense impact on human life and helps one to make important decisions. Since plenty of knowledge and valuable information is available on the internet therefore many users read review information given on web to take decisions such as buying products, watching movies, going to restaurants etc. Reviews contain user opinion about the product, service, event or topic. It is difficult for web users to read and understand the contents from large number of reviews. Whenever any detail is required in the document, this can be achieved by many probabilistic topic models. A topic model provides a generative model for documents and it defines a probabilistic scheme by which documents can be achieved. Topic model is an Integration of acquaintance and these acquaintances are blended with theme, where a theme is a fusion of terms. We describe Latent Dirichlet Markov Allocation 4 level hierarchical Bayesian Model (LDMA), planted on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which highlights on extracting multiword topics from text data. To retrieve the sentiment of the reviews, along with LDMA we will be using SentiWordNet and will compare our result to LDMA with feature extraction of baseline method of sentiment analysis.

Topic Extraction and Sentiment Classification by using Latent Dirichlet Markov Allocation and SentiWordNet

Online teaching has taken up its importance post-pandemic period. Today, online teaching is considered to be one of the teaching pedagogy. This means every teacher and professor is generating online lecture videos and sharing them for students’ later use. Mostly, the environment for the video creation is in real time either in the live classroom or at home, various environmental noises interfere with the actual speech of the presenter. Therefore, there is a need for identifying the various noises that may be part of the lecture video to assess the quality of the video. Towards this, very few research works are observed. Researchers have worked on additive noises, but identifying convolutional noises is a challenge. We propose to work on the audio signal of the video lectures to identify the positions and durations of various convolutional noises and measure the amount of noise present in the audio part of the video lectures. We used various filters for identifying simultaneous talks, long silences, baby crying, kitchen sounds, and vehicle noises. The average accuracy of the proposed solution in identifying the noises and the noise positions is 97.37%. The MSE of the noise in the audio of each clip varies depending on the various noises present. This defines the quality of the audio in the lecture video.

Audio de-noising and quality assessment for various noises in lecture videos

Fuzzy-based DCKN: Fuzzy-based deep convolutional kronecker network for semantic analysis of summarized video

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