Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR

Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regression and expectation-maximization clustering which helps in proactively detecting potential customers before defection. The proposed approach was evaluated with five others, mostly used algorithms: SOM, MLP, Naïve Bayes, RF, J48. The proposed iKLR outperforms the other algorithms with ROC and PRC of 0.856 and 0.650, respectively.

Genre Familiarity Correlation-Based Recommender Algorithm for New User Cold Start Problem

The advancement of web services paved the way to the accumulation of a tremendous amount of information into the world wide web. The huge pile of information makes it hard for the user to get the required information at the right time. Therefore, to get the right item, recommender systems are emphasized. Recommender algorithms generally act on the user information to render recommendations. In this scenario, when a new user enters the system, it fails in rendering recommendation due to unavailability of user information, resulting in a new user problem. So, in this paper, a movie recommender algorithm is constructed to address the prevailing new user cold start problem by utilizing only movie genres. Unlike other techniques, in the proposed work, familiarity of each movie genre is considered to compute the genre significance value. Based on genre significance value, genre similarity is correlated to render recommendations to a new user. The evaluation of the proposed recommender algorithm on real-world datasets shows that the algorithm performs better than the other similar approaches.

War and Peace

The United States Department of Defense (DoD) designs, constructs, and deploys social and autonomous robots and robotic weapons systems. Military robots are designed to follow the rules and conduct of the professions or roles they emulate, and it is expected that ethical principles are applied and aligned with such roles. The application of these principles appear paramount during the COVID-19 global pandemic, wherein substitute technologies are crucial in carrying out duties as humans are more restrained due to safety restrictions. This article seeks to examine the ethical implications of the utilization of military robots. The research assesses ethical challenges faced by the United States DoD regarding the use of social and autonomous robots in the military. The authors provide a summary of the current status of these lethal autonomous and social military robots, ethical and moral issues related to their design and deployment, a discussion of policies, and the call for an international discourse on appropriate governance of such systems.

Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions

Facial expression recognition is a human emotion classification problem attracting much attention from scientific research. Classifying human emotions can be a challenging task for machines. However, more accurate results and less execution time are still the issues when extracting features of human emotions. To cope with these challenges, the authors propose an automatic system that provides users with a well-adopted classifier for recognizing facial expressions in a more accurate manner. The system is based on two fundamental machine learning stages, namely feature selection and feature classification. Feature selection is realized by active shape model (ASM) composed of landmarks while the feature classification algorithm is based on seven well-known classifiers. The authors have used CK+ dataset, implemented and tested seven classifiers to find the best classifier. The experimental results show that quadratic classifier (DA) provides excellent performance, and it outperforms the other classifiers with the highest recognition rate of 100% for the same dataset.

Arabic Biomedical Community Question Answering Based on Contextualized Embeddings

Community question answering has become increasingly important as they are practical for seeking and sharing information. Applying deep learning models often leads to good performance, but it requires an extensive amount of annotated data, a problem exacerbated for languages suffering a scarcity of resources. Contextualized language representation models have gained success due to promising results obtained on a wide array of downstream natural language processing tasks such as text classification, textual entailment, and paraphrase identification. This paper presents a novel approach by fine-tuning contextualized embeddings for a medical domain community question answering task. The authors propose an architecture combining two neural models powered by pre-trained contextual embeddings to learn a sentence representation and thereafter fine-tuned on the task to compute a score used for both ranking and classification. The experimental results on SemEval Task 3 CQA show that the model significantly outperforms the state-of-the-art models by almost 2% for the '16 edition and 1% for the '17 edition.

DeTER Framework

The use of health apps on mobile devices by healthcare providers and receivers (patients) is proliferating. This has elevated cybersecurity concerns owing to the transmittal of personal health information through the apps. Research literature has mostly focused on the technology aspects of cybersecurity in mobile healthcare. It is equally important to focus on the ethical and regulatory perspectives. This article discusses cybersecurity concerns in mobile healthcare from the ethical perspective, the regulatory/compliance perspective, and the technology perspective. The authors present a comprehensive framework (DeTER) that integrates all three perspectives through which cybersecurity concerns in mobile healthcare could be viewed, understood, and acted upon. Guidance is provided with respect to leveraging the framework in the decision-making process that occurs during the system development life cycle (SDLC). Finally, the authors discuss a case applying the framework to a situation involving the development of a contact tracing mobile health app for pandemics such as COVID-19.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

Evaluating Recommender Systems

Replicating the results of the recommender system's evaluation is one of the main concerns in the area. This paper discusses this issue from different angles: 1) It investigates the uniformity of recommenders' evaluation designs presented in practice and their consistency with the theoretical side. 2) It highlights some of the issues and challenges that face recommenders' evaluators. 3) It provides stepwise guidelines for offline evaluation settings. A quantitative study of articles published in the last decade is studied. The search process is a manual search for a conference and a random search of journals. The results show a lack of uniformity and consistency in presenting the evaluation methods. Most of the articles miss at least one evaluation aspect (i.e., some aspects are not presented in the article). These discrepancies and the wide variety of evaluation settings lead to non-replicable experiments. To mitigate this issue, the paper proposes the recommender evaluation guidelines (REval), which presents a roadmap for recommender systems' evaluators.

Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques

This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.

Analysing Twitter Data for Phishing Tweets Identification

Detecting threats like adult, violent, and phishing tweets on online social networks is a crucial issue in recent years. The aim of the work is to identify phishing content from the users' perspective in real-time tweets. To outline such content comprehensively, lexicon analysis with sentiments are encapsulated to investigate tweets that yield phishing dynamic keywords, while some features and parameters are altered to optimize the performance. To support the preliminary study, the approach is rigorously designed to assemble users' opinions on completely different classes of phishing content. Each direct and indirect opinions as well as recently projected opinions are listed to characterize all sorts of phishing content. The authors use word level analysis with sentiments to build keyword blacklist lexicons. High promising results and high level of accuracy and performance are obtained experimentally if compared with the alternative algorithms.