The future of software engineering: Visions of 2025 and beyond

In the current technological scenario of the industry and businesses, there has been increasing need of software within systems and also an increasing demand being put onto software-intensive systems. This in effect will lead to a significant evolution of software engineering processes over the next twenty years. This is due to the fact of emerging technological advancements like Industry 4.0 and Internet of Things in the IT field, among other new developments. This paper addresses and tries to analyses the key research challenges being faced by the software engineering field and articulates information that is derived from the key research specializations within software engineering. The paper analyses the past and current trends in software engineering. The future of software engineering is also looked with respect to Industry 4.0 which including emerging technological platforms like Internet of Things. The societal impact aspect of future trends in software engineering is also addressed in this paper

Performance analysis of sentiments in Twitter dataset using SVM models

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models

Impact of nonlinear chemical reaction and melting heat transfer on an MHD nanofluid flow over a thin needle in porous media

© 2019 by the authors. A novel mathematical model is envisioned discussing the magnetohydrodynamics (MHD) steady incompressible nanofluid flow with uniform free stream velocity over a thin needle in a permeable media. The flow analysis is performed in attendance of melting heat transfer with nonlinear chemical reaction. The novel model is examined at the surface with the slip boundary condition. The compatible transformations are affianced to attain the dimensionless equations system. Illustrations depicting the impact of distinct parameters versus all involved profiles are supported by requisite deliberations. It is perceived that the melting heat parameter has a declining effect on temperature profile while radial velocity enhances due to melting

Cyber physical systems: A smart city perspective

Cyber-physical system (CPS) is a terminology used to describe multiple systems of existing infrastructure and manufacturing system that combines computing technologies (cyber space) into the physical space to integrate human interaction. This paper does a literature review of the work related to CPS in terms of its importance in today’s world. Further, this paper also looks at the importance of CPS and its relationship with internet of things (IoT). CPS is a very broad area and is used in variety of fields and some of these major fields are evaluated. Additionally, the implementation of CPS and IoT is major enabler for smart cities and various examples of such implementation in the context of Dubai and UAE are researched. Finally, security issues related to CPS in general are also reviewed

Recent meta-heuristic algorithms with a novel premature covergence method for determining the parameters of pv cells and modules

Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes

Retinal diseases classification based on hybrid ensemble deep learning and optical coherence tomography images

Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging technique widely used in clinical practice to depict the structure of the retina. Over the past few decades, ophthalmologists have used OCT to diagnose, monitor, and treat retinal diseases. However, manual analysis of the complicated retinal layers using two colors, black and white, is time consuming. Although ophthalmologists have more experience, their results may be prone to erroneous diagnoses. Therefore, in this study, we propose an automatic method for diagnosing five retinal diseases based on the use of hybrid and ensemble deep learning (DL) methods. DL extracts a thousand constitutional features from images as features for training classifiers. The machine learning method classifies the extracted features and fuses the outputs of the two classifiers to improve classification performance. The distribution probabilities of two classifiers of the same class are aggregated; then, class prediction is made using the class with the highest probability. The limited dataset is resolved by the fine-tuning of classification knowledge and generating augmented images using transfer learning and data augmentation. Multiple DL models and machine learning classifiers are used to access a suitable model and classifier for the OCT images. The proposed method is trained and evaluated using OCT images collected from a hospital and exhibits a classification accuracy of 97.68% (InceptionResNetV2, ensemble: Extreme gradient boosting (XG-Boost) and k-nearest neighbor (k-NN). The experimental results show that our proposed method can improve the OCT classification performance; moreover, in the case of a limited dataset, the proposed method is critical to develop accurate classifications

Autonomous vehicles: A study of implementation and security

Autonomous vehicles have been invented to increase the safety of transportation users. These vehicles can sense their environment and make decisions without any external aid to produce an optimal route to reach a destination. Even though the idea sounds futuristic and if implemented successfully, many current issues related to transportation will be solved, care needs to be taken before implementing the solution. This paper will look at the pros and cons of implementation of autonomous vehicles. The vehicles depend highly on the sensors present on the vehicles and any tampering or manipulation of the data generated and transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks against the different type of sensors on-board an autonomous vehicle are covered

Respiratory rate derived from smartphone-camera-acquired pulse photoplethysmographic signals

A method for deriving respiratory rate from smartphone-camera-acquired pulse photoplethysmographic (SCPPG) signal is presented. Our method exploits respiratory information by examining the pulse wave velocity and dispersion from the SCPPG waveform and we term these indices as the pulse width variability (PWV). A method to combine information from several derived respiration signals is also presented and it is used to combine PWV information with other methods such as pulse amplitude variability (PAV), pulse rate variability (PRV), and respiration-induced amplitude and frequency modulations (AM and FM) in SCPPG signals Evaluation is performed on a database containing SCPPG signals recorded from 30 subjects during controlled respiration experiments at rates from 0.2 to 0.6 Hz with an increment of 0.1 Hz, using three different devices: iPhone 4S, iPod 5, and HTC One M8. Results suggest that spontaneous respiratory rates (0.2–0.4 Hz) can be estimated from SCPPG signals by the PWV- and PRVbased methods with low relative error (median of order 0.5% and interquartile range of order 2.5%). The accuracy can be improved by combining PWV and PRV with other methods such as PAV, AM and/or FM methods. Combination of these methods yielded low relative error for normal respiratory rates, and Institute of Physics and Engineering in Medicine maintained good performance at higher rates (0.5–0.6 Hz) when using the iPhone 4S or iPod 5 devices