Urban spaces have a great impact on how people's emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation ...approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture.
Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing ...feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC’20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.
•First hybrid Deep Learning approach applied to environmental and on-body sensor data.•Deep-learning is effective in human emotion classification.•Multi-model data fusion approach performs better ...than single modality classification.•Hybrid approach with combined CNN and LSTM exceed the performance of CNN alone.
The detection and monitoring of emotions are important in various applications, e.g., to enable naturalistic and personalised human-robot interaction. Emotion detection often require modelling of various data inputs from multiple modalities, including physiological signals (e.g., EEG and GSR), environmental data (e.g., audio and weather), videos (e.g., for capturing facial expressions and gestures) and more recently motion and location data. Many traditional machine learning algorithms have been utilised to capture the diversity of multimodal data at the sensors and features levels for human emotion classification. While the feature engineering processes often embedded in these algorithms are beneficial for emotion modelling, they inherit some critical limitations which may hinder the development of reliable and accurate models. In this work, we adopt a deep learning approach for emotion classification through an iterative process by adding and removing large number of sensor signals from different modalities. Our dataset was collected in a real-world study from smart-phones and wearable devices. It merges local interaction of three sensor modalities: on-body, environmental and location into global model that represents signal dynamics along with the temporal relationships of each modality. Our approach employs a series of learning algorithms including a hybrid approach using Convolutional Neural Network and Long Short-term Memory Recurrent Neural Network (CNN-LSTM) on the raw sensor data, eliminating the needs for manual feature extraction and engineering. The results show that the adoption of deep-learning approaches is effective in human emotion classification when large number of sensors input is utilised (average accuracy 95% and F-Measure=%95) and the hybrid models outperform traditional fully connected deep neural network (average accuracy 73% and F-Measure=73%). Furthermore, the hybrid models outperform previously developed Ensemble algorithms that utilise feature engineering to train the model average accuracy 83% and F-Measure=82%)
The Parameter estimation of solar cell models is considered an important problem in the computational simulation and design of photovoltaic (PV) systems. In this paper, the PV parameters of single, ...double, and triple-diode models are extracted and tested under different environmental conditions. The parameter estimation of the three models is presented as an optimization process with an objective function to reduce the difference between the measured and calculated data. Moreover, a new optimization algorithm for extracting the PV parameters of the single, double, and three-diode models called the Manta Ray Foraging Optimization (MRFO) algorithm has been proposed. The results show that the obtained parameters are the optimal values and give the least difference between the measured and calculated data compared with other algorithms.
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•Manta ray foraging optimization (MRFO)is used to determine the variables of the Photovoltaic (PV).•MRFO can get the optimal values of the PV parameters of the single, the double and the three diode models.•A comparison has been done between the MRFO with other metaheuristic algorithms.•The results show that MRFO gave the least difference between the calculated values and the actual values.
Metaheuristic applications for information retrieval research are limited in spite of the importance of this problem domain. Ranking the retrieved documents based on their importance is a vital issue ...for the scientific and industrial communities. This paper proposes a novel variable neighborhood search (VNS) algorithm with adaptation based on an objective function for the learning to rank (LTR) problem. VNS is a global optimum metaheuristic algorithm that has been engaged to evolve the optimal solutions for heuristic problems based on exploring better neighbor solutions from the current one. The changes from the current to the next optimal solution are made during the perturbation stage to identify the global optimal solutions. The exploration procedure has been made through various mutation step sizes, whereas the exploitation process has been done by checking the quality of the evolved solutions using the fitness function. This research proposes a novel version of VNS based on four random probability distributions with gradient ascent. In addition to using the traditional random generator with gradient ascent for modifying the mutated genes of the neighborhood candidate solution in the following evolving iteration. This novel method in LTR is called gradient variable neighborhood (GVN). In the experiments, we utilized Microsoft Bing search (MSLR-WEB30K), Yahoo, and TREC Million Queries Competitions in 2008 and 2007 (LETOR 4) datasets. From the findings of the results, we can deduce that the GVN method outperformed recent studies on LTR methods.
Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as ...text, speech, body gestures or even physiologically by physiological body responses. Emotion detection enables many applications such as adaptive user interfaces, interactive games, and human robot interaction and many more. The availability of advanced technologies such as mobiles, sensors, and data analytics tools led to the ability to collect data from various sources, which enabled researchers to predict human emotions accurately. Most current research uses them in the lab experiments for data collection. In this work, we use direct and real time sensor data to construct a subject-independent (generic) multi-modal emotion prediction model. This research integrates both on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: (1) Collecting a multi-modal data set including environmental, body responses, and emotions. (2) Creating subject-independent Predictive models of emotional states based on fusing environmental and physiological variables. (3) Assessing ensemble learning methods and comparing their performance for creating a generic subject-independent model for emotion recognition with high accuracy and comparing the results with previous similar research. To achieve that, we conducted a real-world study “in the wild” with physiological and mobile sensors. Collecting the data-set is coming from participants walking around Minia university campus to create accurate predictive models. Various ensemble learning models (Bagging, Boosting, and Stacking) have been used, combining the following base algorithms (K Nearest Neighbor KNN, Decision Tree DT, Random Forest RF, and Support Vector Machine SVM) as base learners and DT as a meta-classifier. The results showed that, the ensemble stacking learner technique gave the best accuracy of 98.2% compared with other variants of ensemble learning methods. On the contrary, bagging and boosting methods gave (96.4%) and (96.6%) accuracy levels respectively.
In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to ...add metadata and to label sensor data streams. Mobile self-report techniques are used to record user ratings of their experiences during structured studies, instead of traditional paper-based surveys. These techniques can be timely and convenient when data are collected “in the wild”. This paper proposes three new viable methods for mobile self-reporting projects and in real-life settings such as recording weather information or urban noise mapping. These techniques are Volume Buttons control, NFC-on-Body, and NFC-on-Wall. This work also provides an experimental and comparative analysis of various self-report techniques regarding user preferences and submission rates based on a series of user experiments. The statistical analysis of our data showed that pressing screen buttons and screen touch allowed for higher labelling rates, while Volume Buttons proved to be more valuable when users engaged in other activities, e.g. while walking. Similarly, based on participants’ preferences, we found that NFC labelling was also an easy and intuitive technique when used in the context of self-reporting and place-tagging. Our hope is that by reviewing current self-reporting interfaces and user requirements, we will be able to enable new forms of self-reporting technologies that were not possible before.
Heart disease is one of the first causes of death worldwide. This paper presents a real-time system for predicting heart disease from medical data streams that describe a patient’s current health ...status. The main goal of the proposed system is to find the optimal machine learning algorithm that achieves high accuracy for heart disease prediction. Two types of features selection algorithms, univariate feature selection and Relief, are used to select important features from the dataset. We compared four types of machine learning algorithms; Decision Tree, Support Vector Machine, Random Forest Classifier, and Logistic Regression Classifier with the selected features as well as full features. We apply hyperparameter tuning and cross-validation with machine learning to enhance accuracy. One core merit of the proposed system is able to handle Twitter data streams that contain patients’ data efficiently. This is done by integrating Apache Kafka with Apache Spark as the underlying infrastructure of the system. The results show the random forest classifier outperforms the other models by achieving the highest accuracy at 94.9%.
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•Developing a real-time to extract knowledge related to heart diseases from user tweets streaming.•Finding the optimal machine learning algorithm that achieves the best accuracy for heart disease.•Applying feature selection to select the most important features from the dataset to build the model.
Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data ...source for machine learning research in terms of analysis, prediction, knowledge extraction, and opinions. Sentiment analysis is a text analysis method that has gained further significance due to social networks’ emergence. Therefore, this paper introduces a real-time system for sentiment prediction on Twitter streaming data for tweets about the coronavirus pandemic. The proposed system aims to find the optimal machine learning model that obtains the best performance for coronavirus sentiment analysis prediction and then uses it in real-time. The proposed system has been developed into two components: developing an offline sentiment analysis and modeling an online prediction pipeline. The system has two components: the offline and the online components. For the offline component of the system, the historical tweets’ dataset was collected in duration 23/01/2020 and 01/06/2020 and filtered by #COVID-19 and #Coronavirus hashtags. Two feature extraction methods of textual data analysis were used, n-gram and TF-ID, to extract the dataset’s essential features, collected using coronavirus hashtags. Then, five regular machine learning algorithms were performed and compared: decision tree, logistic regression, k-nearest neighbors, random forest, and support vector machine to select the best model for the online prediction component. The online prediction pipeline was developed using Twitter Streaming API, Apache Kafka, and Apache Spark. The experimental results indicate that the RF model using the unigram feature extraction method has achieved the best performance, and it is used for sentiment prediction on Twitter streaming data for coronavirus.
Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and ...deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applications such as marketing, human–robot interaction, electronic games, E-learning, and many more. It is essential for any application requiring to know the emotional state of the person and act accordingly. The automated methods for recognizing emotions use many modalities such as facial expressions, written text, speech, and various biosignals such as the electroencephalograph, blood volume pulse, electrocardiogram, and others to recognize emotions. The signals can be used individually(uni-modal) or as a combination of more than one modality (multi-modal). Most of the work presented is in laboratory experiments and personalized models. Recent research is concerned about in the wild experiments and creating generic models. This study presents a comprehensive review and an evaluation of the state-of-the-art methods for AHER employing machine learning from a computer science perspective and directions for future research work.
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