A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-007155.pdf

Limits of predictability in human mobility

In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. The main purpose of this proposed work is to develop a system that can determine whether a tweet is “spam” or “ham” and evaluate the emotion of the tweet. The extracted features after preprocessing the tweets are classified using various classifiers, namely, decision tree, logistic regression, multinomial naïve Bayes, support vector machine, random forest, and Bernoulli naïve Bayes for spam detection. The stochastic gradient descent, support vector machine, logistic regression, random forest, naïve Bayes, and deep learning methods, namely, simple recurrent neural network (RNN) model, long short-term memory (LSTM) model, bidirectional long short-term memory (BiLSTM) model, and 1D convolutional neural network (CNN) model are used for sentiment analysis. The performance of each classifier is analyzed. The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets with a particular sentiment.

/pdf/real-time-twitter-spam-detection-and-sentiment-analysis-1f9t34fu.pdf

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

Advancements in recent networking and information technology have always been a natural phenomenon. The exponential amount of data generated by the people in their day-to-day lives results in the rise of Big Data Analytics (BDA). Cognitive computing is an Artificial Intelligence (AI) based system that can reduce the issues faced during BDA. On the other hand, Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets. The application of SA on big data finds it useful for businesses to take commercial benefits insight from text-oriented content. In this view, this paper presents new cognitive computing with the big data analysis tool for SA. The proposed model involves various process such as pre-processing, feature extraction, feature selection and classification. For handling big data, Hadoop Map Reduce tool is used. The proposed model initially undergoes pre-processing to remove the unwanted words. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a Binary Brain Storm Optimization (BBSO) algorithm is being used for the Feature Selection (FS) process and thereby achieving improved classification performance. Moreover, Fuzzy Cognitive Maps (FCMs) are used as a classifier to classify the incidence of positive or negative sentiments. A comprehensive experimental results analysis ensures the better performance of the presented BBSO-FCM model on the benchmark dataset. The obtained experimental values highlights the improved classification performance of the proposed BBSO-FCM model in terms of different measures.

An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

/pdf/a-review-on-deep-learning-techniques-for-iot-data-1ds41b7s.pdf

A Review on Deep Learning Techniques for IoT Data

Advancements in recent networking and information technology have always been a natural phenomenon. The exponential amount of data generated by the people in their day-to-day lives results in the rise of Big Data Analytics (BDA). Cognitive computing is an Artificial Intelligence (AI) based system that can reduce the issues faced during BDA. On the other hand, Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets. The application of SA on big data finds it useful for businesses to take commercial benefits insight from text-oriented content. In this view, this paper presents new cognitive computing with the big data analysis tool for SA. The proposed model involves various process such as pre-processing, feature extraction, feature selection and classification. For handling big data, Hadoop Map Reduce tool is used. The proposed model initially undergoes pre-processing to remove the unwanted words. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a Binary Brain Storm Optimization (BBSO) algorithm is being used for the Feature Selection (FS) process and thereby achieving improved classification performance. Moreover, Fuzzy Cognitive Maps (FCMs) are used as a classifier to classify the incidence of positive or negative sentiments. A comprehensive experimental results analysis ensures the better performance of the presented BBSO-FCM model on the benchmark dataset. The obtained experimental values highlights the improved classification performance of the proposed BBSO-FCM model in terms of different measures. 

Abstract Detecting dangerous illnesses connected to the skin organ, particularly malignancy, requires the identification of pigmented skin lesions. Image detection techniques and computer classification capabilities can boost skin cancer detection accuracy. The dataset used for this research work is based on the HAM10000 dataset which consists of 10015 images. The proposed work has chosen a subset of the dataset and performed augmentation. A model with data augmentation tends to learn more distinguishing characteristics and features rather than a model without data augmentation. Involving data augmentation can improve the accuracy of the model. But that model cannot give significant results with the testing data until it is robust. The k-fold cross-validation technique makes the model robust which has been implemented in the proposed work. We have analyzed the classification accuracy of the Machine Learning algorithms and Convolutional Neural Network models. We have concluded that Convolutional Neural Network provides better accuracy compared to other machine learning algorithms implemented in the proposed work. In the proposed system, as the highest, we obtained an accuracy of 95.18% with the CNN model. The proposed work helps early identification of seven classes of skin disease and can be validated and treated appropriately by medical practitioners. 

/pdf/skin-lesion-classification-of-dermoscopic-images-using-580aq1xl.pdf

Skin lesion classification of dermoscopic images using machine learning and convolutional neural network

Many researchers have contributed to automate the optical character recognition. But handwritten character recognition is still an uncompleted task. In this paper we are proposing two techniques to recognize handwritten Kannada script, which yields high accuracy compared to previous works. There are lot of challenges in recognizing handwritten Kannada scripts. Few of the challenges include: each person have their own handwriting, there is no uniform spacing between alphabets, words and lines. Another main problem when it comes to Kannada language is that there is no large dataset available to train the recognition system, and it is challenging to write all combinations of each alphabet in Kannada script. In the proposed work, we have gathered the handwritten training set from the Web and from the students of our campus and segmented each letter. We have proposed two methods to recognize the handwritten Kannada characters. The first techniques is by Tesseract tool, and second is by using Convolution Neural Network (CNN). With Tesseract tool we have achieved 86% accuracy and through Convolution Neural Network we achieved87% accuracy although it might improve with the data set chosen and further enhanced image processing. The main idea behind this work is to extract text from the scanned images, identify the Kannada letters in it accurately and display or store it for further usage.

Kannada Handwritten Script Recognition using Machine Learning Techniques

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. In this paper, we have concentrated on providing the opinion on the particular product using the Twitter data. There are millions of reviews on single product and it would be impossible for the customer or the organization to read each review and judge the quality of the product. This implementation paper provides the opinion mining on particular product based on reviews. The work includes determination of positivity, negativity of tweets and provides overall percentage of positive, negative and neutral tweets. The main idea behind this work is that the customer should automatically get suggestion about the product based on previous tweets. This implementation paper also provides effective decision making opinion to the customer and also provides feedback to the company to improve their product and business.

Analysis of product Twitter data though opinion mining

Mobility prediction is a technique in which the future location of a user is identified in a given network. Mobility prediction provides solutions to many day-to-day life problems. It helps in seamless handovers in wireless networks to provide better location based services and to recalculate paths in Mobile Ad hoc Networks (MANET). In the present study, a framework is presented which predicts user mobility in presence and absence of mobility history. Naive Bayesian classification algorithm and Markov Model are used to predict user future location when user mobility history is available. An attempt is made to predict user future location by using Short Message Service (SMS) and instantaneous Geological coordinates in the absence of mobility patterns. The proposed technique compares the performance metrics with commonly used Markov Chain model. From the experimental results it is evident that the techniques used in this work gives better results when considering both spatial and temporal information. The proposed method predicts user’s future location in the absence of mobility history quite fairly. The proposed work is applied to predict the mobility of medical rescue vehicles and social security systems.

Roshan Fernandes

Papers

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

Skin lesion classification of dermoscopic images using machine learning and convolutional neural network

Kannada Handwritten Script Recognition using Machine Learning Techniques

Analysis of product Twitter data though opinion mining

A New Approach to Predict user Mobility Using Semantic Analysis and Machine Learning.