Agile Software Development: Methodologies and Trends

Software engineering is a discipline that undergone many improvements that aims to keep up with the new advancements in technologies and the modern business requirements through developing effective approaches to reach the final software product, agile software development is one of these successful approaches. Agile software development is a lightweight approach that was proposed to overcome the convolutional development methods’ limitations and to reduce the overhead and the cost while providing flexibility to adopt the changes in requirements at any stage, this is done by managing the tasks and their coordination through a certain set of values and principles.In this work, a comprehensive review that outlines the main agile values and principles, and states the key differences that distinguish agile methods over the traditional ones are presented. Then a discussion of the most popular agile methodologies; their life cycles, their roles, and their advantages and disadvantages are outlined. The recent state of art trends that adopts agile development especially in cloud computing, big data, and coordination are also explored. And finally, this work highlights how to choose the best suitable agile methodology that must be selected according to the task at hand, how sensitive the product is and the organization structure.</p

HcLSH: A Novel Non-Linear Monotonic Activation Function for Deep Learning Methods

Activation functions are essential components in any neural network model; they play a crucial role in determining the network&#x2019;s expressive power through their introduced non-linearity. Rectified Linear Unit (ReLU) has been the famous and default choice for most deep neural network models because of its simplicity and ability to tackle the vanishing gradient problem that faces backpropagation optimization. However, ReLU introduces other challenges that hinder its performance; bias shift and dying neurons in the negative region. To address these problems, this paper presents a novel composite monotonic, zero-centered, semi-saturated activation function called Hyperbolic cosine Linearized SquasHing function (HcLSH) with partial gradient-based sparsity HcLSH owns many desirable properties, such as considering the contribution of the negative values of neurons while having a smooth output landscape to enhance the gradient flow during training. Furthermore, the regularization effect resulting from the self-gating property of the positive region of HcLSH reduces the risk of model overfitting and ensures learning more robust expressive representations. An extensive set of experiments and comparisons is conducted that includes four popular image classification datasets, seven deep network architectures, and ten state-of-the-art activation functions. HcLSH exhibited the Top-1 and Top-3 testing accuracy results in 20 and 25 out of 28 conducted experiments, respectively, suppressing the widely used ReLU that achieved 2 and 5, and the reputable Mish that achieved 0 and 5 Top-1 and Top-3 testing accuracy results, respectively. HcLSH attained improvements over ReLU, ranging from 0.2&#x0025; to 96.4&#x0025; in different models and datasets. Statistical results demonstrate the significance of the enhanced performance achieved by our proposed HcLSH activation function compared to the competitive activation functions in various datasets and models regarding the testing loss Furthermore, the ablation study further verifies the proposed activation function&#x2019;s robustness, stability, and adaptability for the different model parameter

An Enhanced Multi-Phase Stochastic Differential Evolution Framework for Numerical Optimization

Real-life problems can be expressed as optimization problems. These problems pose a challenge for researchers to design efficient algorithms that are capable of finding optimal solutions with the least budget. Stochastic Fractal Search (SFS) proved its powerfulness as a metaheuristic algorithm through the large research body that used it to optimize different industrial and engineering tasks. Nevertheless, as with any meta-heuristic algorithm and according to the 'No Free Lunch' theorem, SFS may suffer from immature convergence and local minima trap. Thus, to address these issues, a popular Differential Evolution variant called Success-History based Adaptive Differential Evolution (SHADE) is used to enhance SFS performance in a unique three-phase hybrid framework. Moreover, a local search is also incorporated into the proposed framework to refine the quality of the generated solution and accelerate the hybrid algorithm convergence speed. The proposed hybrid algorithm, namely eMpSDE, is tested against a diverse set of varying complexity optimization problems, consisting of well-known standard unconstrained unimodal and multimodal test functions and some constrained engineering design problems. Then, a comparative analysis of the performance of the proposed hybrid algorithm is carried out with the recent state of art algorithms to validate its competitivity

A comprehensive review of the deep learning-based tumor analysis approaches in histopathological images: segmentation, classification and multi-learning tasks

Medical Imaging has become a vital technique that has been embraced in the diagnosis and treatment process of cancer. Histopathological slides, which microscopically examine the suspicious tissue, are considered the golden standard for tumor prognosis and diagnosis. This excellent performance caused a sudden and growing interest in digitizing these slides to generate Whole Slide Images (WSI). However, analyzing WSI is a very challenging task due to the multiple-resolution, large-scale nature of these images. Therefore, WSI-based Computer-Aided Diagnosis (CAD) analysis gains increasing attention as a secondary decision support tool to enhance healthcare by alleviating pathologists’ workload and reducing misdiagnosis rates. Recent revolutionized deep learning techniques are promising and have the potential to achieve efficient automatic representation of WSI features in a data-driven manner. Thus, in this survey, we focus mainly on deep learning-based CAD systems in the context of tumor analysis in histopathological images, i.e., segmentation and classification of tumor regions. We present a visual taxonomy of deep learning approaches that provides a systematic structure to the vast number of diverse models proposed until now. We sought to identify challenges that face the automation of histopathological analysis, the commonly used public datasets, and evaluation metrics and discuss recent methodologies for addressing them through a systematic examination of presented deep solutions. The survey aims to highlight the existing gaps and limitations of the recent deep learning-based WSI approaches to explore the possible avenues for potential enhancements

An iterative cyclic tri-strategy hybrid stochastic fractal with adaptive differential algorithm for global numerical optimization

Many real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms