Implementation of ANN in Software Effort Estimation: Boundary Value Effort Forecast: A novel Artificial Neural Networks model to improve the accuracy of Effort Estimation in Software Development Projects

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementSoftware Development consistently accommodates a variety of unstable scenarios. Good planning always stands behind well-defined requirements. Hence, the consistency of the effort estimation plays a special role in the traditional Business-Consumer relationship. While the proposed models may provide high accuracy in predicting specific data sets, it’s still difficult for IT specialists/organizations to find the best method for evaluating certain functionalities. The challenge of the project; initiated programming language, project infrastructure, and/or staff experimentation are just a few of the reasons that lead to inequality in these terms. Conceptually, the planned work going to explicate the main correlations. It will contain historical background - as to how was the industrial lifecycle before pre-processing progress/what was the necessity for them to exist, as well as modern usage area of BPM and Project Management – like how managers and owners’ moves are intending to keep the consumer’s satisfaction in higher level while increasing the revenue. Taking the most failure causes of projects into consideration, the research will capture some components of Software Project Management to clarify developed approaches and their advantages and/or disadvantages. The study may also lead somehow to the Business Process Management to see the alignments of required tasks in a rigorous way. The research is generally intending to define the key features of the Project Effort Estimation as usage of the datasets, evaluating the architectures, etc. The investigation also aims to find effective causes of poor effort estimation and analyze how those improvable points may be developed to ensure a highly accurate Artificial Neural Networks model

5G Outlook – Innovations and Applications

5G Outlook - Innovations and Applications is a collection of the recent research and development in the area of the Fifth Generation Mobile Technology (5G), the future of wireless communications. Plenty of novel ideas and knowledge of the 5G are presented in this book as well as divers applications from health science to business modeling. The authors of different chapters contributed from various countries and organizations. The chapters have also been presented at the 5th IEEE 5G Summit held in Aalborg on July 1, 2016. The book starts with a comprehensive introduction on 5G and its need and requirement. Then millimeter waves as a promising spectrum to 5G technology is discussed. The book continues with the novel and inspiring ideas for the future wireless communication usage and network. Further, some technical issues in signal processing and network design for 5G are presented. Finally, the book ends up with different applications of 5G in distinct areas. Topics widely covered in this book are: • 5G technology from past to present to the future• Millimeter- waves and their characteristics• Signal processing and network design issues for 5G• Applications, business modeling and several novel ideas for the future of 5

5G Outlook – Innovations and Applications

5G Outlook - Innovations and Applications is a collection of the recent research and development in the area of the Fifth Generation Mobile Technology (5G), the future of wireless communications. Plenty of novel ideas and knowledge of the 5G are presented in this book as well as divers applications from health science to business modeling. The authors of different chapters contributed from various countries and organizations. The chapters have also been presented at the 5th IEEE 5G Summit held in Aalborg on July 1, 2016. The book starts with a comprehensive introduction on 5G and its need and requirement. Then millimeter waves as a promising spectrum to 5G technology is discussed. The book continues with the novel and inspiring ideas for the future wireless communication usage and network. Further, some technical issues in signal processing and network design for 5G are presented. Finally, the book ends up with different applications of 5G in distinct areas. Topics widely covered in this book are: • 5G technology from past to present to the future• Millimeter- waves and their characteristics• Signal processing and network design issues for 5G• Applications, business modeling and several novel ideas for the future of 5

Supporting Second Chances: Employment Strategies for Reentry Programs

The Second Chance Act supports a range of reentry programs around the country, designed to help those returning from jail or prison make a successful transition to life on the outside. In 2008, the Annie E. Casey Foundation commissioned Public/Private Ventures (P/PV) to create a resource that would be useful for Second Chance Act grantees as they develop employment strategies, by distilling lessons from research on a range of employment programs. "Supporting Second Chances" offers concrete suggestions for practitioners, based on a review of relevant literature and P/PV's own extensive experience with reentry and workforce development research and programming. The guide explores strategies in three major areas:Services aimed at helping people find immediate employment;Services that provide paid job experiences to participants; andServices that help people gain occupational skills.For each area, we provide: an overview of the approach, including its history and a brief definition; a high-level summary of the most recent and rigorous research available about the approach; an example of the approach in action; key "takeaways" for Second Chance Act grantees and other programs serving formerly incarcerated individuals; and where to go to learn more.Since the ultimate success of an employment strategy may hinge on a range of additional supports, the guide also features a section called "Beyond Getting a Job," which presents three approaches to help formerly incarcerated individuals get the most out of their paychecks and move into better jobs. The final section synthesizes lessons drawn from across the studies reviewed for the guide

An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

[EN] Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.This work was supported in part by the National Research Foundation of Korea Grant Funded by the Korea Government (MSIT) under Grant 2019M3F2A1073179; in part by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" Within the Project under Grant TIN2017-84802-C2-1-P; and in part by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET Joint Activities and Beyond) Project ERANETMED3-227 SMARTWATIR.Han, T.; Muhammad, K.; Hussain, T.; Lloret, J.; Baik, SW. (2021). An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things. 8(5):3170-3179. https://doi.org/10.1109/JIOT.2020.3013306S317031798

A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches

Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities