Enabling technologies and sustainable smart cities

Blockchain for smart cities: A review of architectures, integration trends and future research directions

Development in the field of electronics and communication set up new paradigm. The need of making the grid smart is increasing due to environmental constrain and effective use of the available electricity. The use of digital information and controls technology to improve reliability, security, and efficiency of the electric grid.

Smart grid

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence/machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2019. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 176 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.

Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Islamic financial markets and global crises: Contagion or decoupling?

The systematic failure of the global equity markets during the recent financial crisis made investors re-evaluate their portfolio constituents. It is argued that equities that comply with the Islamic investment principles perform better than conventional equities during the declining phase of capital markets. The better performance of Islamic investments can be attributed to the Shari'ah based screening criteria that specifically forbids investment in shares of those companies that are excessively leveraged and/or engaged in lending activities. This study investigates the extent to which this claim is valid by comparing the performance of global and regional Islamic equity indices (IEIs) with conventional equity indices during the past decade. The equity indices for such analysis are preferred since it does not account for transaction costs or management skills. A logistic smooth transition autoregressive (LSTAR) model is used to investigate whether the ‘down market’ performance of IEIs differs from conventional indices. The LSTAR is superior to conventional ordinary least squares models since this allows for a smooth transition from the ‘down market’ to the ‘up market’ rather than an abrupt change. The empirical results indicate that IEIs, in general, perform better than conventional indices during the period 2000 to 2012. We do not find any abnormal returns associated with Islamic equity indices on a global basis, however, there is evidence of positive abnormal returns in the case of regional indices from Europe and Asia. Overall, IEIs exhibit lower systematic risk as compared with their benchmark suggesting that any excess performance from Islamic investments stems from the systematic risk that each investment assumes with respect to their benchmark during the declining phase of capital markets. The findings of this study are of interest to both academics and the general investing public since it provides evidence that IEIs are comparatively less risky than their conventional counterpart and thus provide hedging opportunities during the downfall of capital markets.

Matching perception with the reality—Performance of Islamic equity investments

ArASL: Arabic Alphabets Sign Language Dataset.

This study investigates the determinants of return performance of Islamic equity indices (IEIs). Empirical evidence suggests that the selection of securities and rebalancing of funds to comply with Islamic screening standards may result in superior returns for the investing public. We employ an extended four factor dynamic condition correlation GARCH model to a sample of IEIs from different regions for the period 2002–2013. The empirical results indicate a statistically significant difference between IEIs from developed markets and those from emerging markets during the sample period. Findings suggest that Shari'ah screening helps IEIs to select securities of firms that are not financially distressed, are growth oriented and are exhibiting a positive momentum. We further investigated whether Shari'ah-based screening provides any superior market timing ability over conventional benchmark indices using both parametric and non-parametric approaches. Market timing results showed that the majority of IEIs have negative market timing ability reflecting the conservative nature of Islamic equity investments whereby Shari'ah discourages investment in equities of highly leveraged corporations.

The market timing ability and return performance of Islamic equities: An empirical study

Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification of images. In this paper, an enhanced method is presented for glioma MR images classification using hybrid statistical and wavelet features. In the proposed method, 52 features are extracted using the first-order and second-order statistical features (based on the four MRI modalities: Flair, T1, T1c, and T2) in addition to the discrete wavelet transform producing a total of 152 features. The extracted features are applied to the multilayer perceptron (MLP) classifier. The results using the MLP were compared with various known classifiers. The method was tested on the dataset MICCAI BraTS 2015 which is a standard dataset used for research purposes. The proposed hybrid statistical and wavelet features produced 96.72% accuracy for high-grade glioma and 96.04% accuracy for low-grade glioma, which are relatively better compared to the existing studies.

/pdf/enhanced-mr-image-classification-using-hybrid-statistical-1j1i51orid.pdf

Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

The concept of IoT-based Smart Cities has gained momentum in recent years. The research in this domain has focused on modeling key characteristics of future smart cities along with exploring their design and implementation aspects from multiple perspectives. There is, however, a lack of research effort to provide a holistic approach towards modeling smart city services. A comprehensive approach has to view the smart city as a dynamic, uncertain and complex environment where multiple events with varying severity occur in a continuous and non-deterministic manner. These events have to be handled efficiently with the available resources. In this paper, we present a holistic approach to model the probabilistic as well as non-deterministic aspects of the emergency management services of a smart city by using probabilistic model checking. Our proposed model captures the emergency events of varying severity occurring at several locations in a continuous and non-deterministic manner. These events are responded to by a smart emergency management unit (SEMU), which dispatches the required emergency response units (ERUs) to the event location. In addition to modeling a completely automated system, we introduce a human-assisted decision-making process in the model to reduce false alarms. The proposed model can be used to study and evaluate key parameters in designing a smart city emergency management system to meet given service level agreements. We have implemented our proposed model using the PRISM model checker and have conducted several case studies to highlight the effectiveness of our proposed approach for different scenarios of varying complexities.

/pdf/formal-analysis-of-human-assisted-smart-city-emergency-3rjjt3bce3.pdf

Nazeeruddin Mohammad

Papers

Matching perception with the reality—Performance of Islamic equity investments

ArASL: Arabic Alphabets Sign Language Dataset.

The market timing ability and return performance of Islamic equities: An empirical study

Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

Formal Analysis of Human-Assisted Smart City Emergency Services