TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles.

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.

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

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.

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

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.

Perioperative risk factors for pulmonary complications after non-cardiac surgery.

BACKGROUND AND AIMS: Postoperative pulmonary complications (PPCs) lead to increased morbidity, mortality, length of hospital stay, and cost to the patient. This study was conducted to determine the risk factors and assess the incidence of PPC after non-cardiac surgery. MATERIAL AND METHODS: This prospective, observational study was conducted on 1,170 patients undergoing non-cardiac surgery. Details of patient, surgical, and anesthetic factors were collected and patients were followed up for the entire duration of hospital stay for the occurrence of PPC. Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score and the length of hospital stay was noted for all the patients. Regression analysis was used to find the risk factors associated with development of respiratory complications. RESULTS: The incidence of PPC was found to be 59 in 1,170 patients (5%) in our hospital. Multivariate analysis revealed that patients with intermediate and high risk ARISCAT scoring had higher odds of developing PPC. Higher age (>50 years), positive cough test, presence of nasogastric tube, and intraoperative pulmonary complications were identified as independent risk factors associated with the occurrence of PPC. CONCLUSION: We found 5% incidence of PPC in our study. Recognition of the delineated risk factors and routine use of ARISCAT score for preoperative assessment may help identify patients at a higher risk of developing postoperative pulmonary complications.

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

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. Naïve 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.

Polycystic ovary syndrome: a review of cases from general practice.

This paper reports a review of 55 cases of polycystic ovary syndrome by general practice registrars in the Waikato region of New Zealand. In addition to demographic data, presenting symptoms, diagnostic tests, associated conditions and treatment post-diagnosis are discussed. The majority of cases (76%) were first diagnosed by the general practitioner. The review suggests there may be a need for better recording of key diagnostic criteria and that ultrasound is being widely used as a diagnostic test despite local guidelines discouraging its use if other appropriate diagnostic criteria are met.