Web-Based Land Registration Management System: Iraq/Duhok Case Study

In this era, technology is playing a central role in many areas of human life, but the classical hardcopy-based approaches are still being used for land registration. The Internet-based methods provide excellent facilities for overcoming the drawbacks of handwritten-based style and communication among different government sectors. Nowadays, Information and Communication Technology (ICT) is used to build professional electronic systems as big steps towards the electronic government (E-government) system. One of the most critical sections of the E-government is the E-Land-Registration (ELR). Duhok Land Directorate, together with its sub-directorates, works on a considerable amount of data to process. These directorates suffer from the classical hardcopy-based approaches, so building an ELR system will reduce time consumption and paper waste. The improvement of the land registration system will also allow integration with the E-government system. The progress of the land registration will enable communication between the land registration staff on one side and the administration and financial directorates on the other. In this thesis, an efficient ELR system for Duhok land registration is proposed. The services of the database management system cover Employee Registration Module, Estates Registration Module, Operation Type Module, Estate Owners Module, Estate Status Module, View Information Module, and Login Employee Module. HTML, CSS, PHP, MySQL, JavaScript, jQuery, Ajax, and Bootstrap tools were used for the design and implementation stages of the proposed ELR.

Predicting Football Outcomes by Using Poisson Model: Applied to Spanish Primera División

During the past decades, sport, in general, has become one of the most powerful competitions and the most popular in the world. As well as, everyone is waiting for the winner, and who will be the champion in the end in different tournaments. Among these sports, football's popularity is more than all other sports. Football matches results predicting, as well as the champion in various competitions, has been seriously studied in recent years. Moreover, it has become an interesting field for many researchers. In this work, the Poisson model has been presented to predict the winner, draw, and loser from the football matches. The method is applied to the Spanish Primera División (First Division) in 2016-2017; the data has been downloaded from the football-data.co.uk website, which will be used to find the prediction accuracy.

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

Modeling and the main stages of spin coating process: A review

Spin coating is a technique employed for the deposition of uniform thin films of organic materials in the range of micrometer to nanometer on flat substrates. Typically, a small amount of coating material generally as a liquid is dropped over the substrate center, which is either static or spinning at low speed. The substrate is then rotated at the desired speed and the coating material has been spread by centrifugal force. A device that is used for spin coating is termed a spin coater or just a spinner. The substrate continued to spin and the fluid spins off the boundaries of the substrate until the film is reached the required thickness. The thickness and the characteristics of coated layer (film) are depending on the number of rotations per minute (rpm) and the time of rotation. Therefore, a mathematical model is obtained to clarify the prevalent method controlling thin film fabrication. Viscosity and the concentration of (solution) spin coating material are also affecting the thickness of the substrate. This article reviews spin coating techniques including stages in the coating process such as deposition, spin-up, stable fluid outflow (spin-off), and evaporation. Additionally, the main affecting factors on the film thickness in the coating process are reviewed.

Internet of Robotic Things: A Review

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

A Review on Using ANOVA and RSM Modelling in The Glass Powder Replacement of The Concrete Ingredients

The flat glass powder usage instead of sand is convenient in structurally serviceable and environmentally compatible concrete. The deposits of glass powder in fibres cement compounds manufacture may add significant technical, economic and environmental necessities. The cement material and cement replacement by glass powder is chosen as parameters of the concrete. When the waste glass is fined to very fine dust, it demonstrates a cementitious characteristic due to silica content. Statistical methods and techniques are heavily used in glass powder replacement. In this paper, fifteen papers are reviewed and investigated to check the availability of using the statistical and modelling system in discussing the glass powder replacement with some other ingredients results between 2012-2021. We found that most of the papers depended on the ANOVA test to perform their work. Moreover, central composite face-centred (CFC) and Response Surface Methodology (RSM) took a part in the studies. From the numerous replicas, a quadratic prototypical was supplied with waste glass powder in the numbers of the studies that the glass waste powder is the best with its characteristics.

Multimodal Emotion Recognition using Deep Learning

New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

Energy Management for Internet of Things via Distributed Systems

The distributed energy system (DES) architecture is subject to confusion about renewable energy limits, primary energy supply and energy carriers' costs. For the grid to use unreliable electricity sources, the end-user's on-demand presence in the intelligent energy management context is essential. The participation of end-users could influence the management of the system and the volatility of energy prices. By delivering auxiliary services using demand side-resource to increase system reliability, robust planning, constraint control and scheduling, consumers may support grid operators. The optimized approach to managing energy resources enhances demand response to renewable energy sources integrally, controls the demand curve with load versatility as the system requires it. The opportunity to adjust/regulate the charging profile by choosing a particular device. This article discusses a literature and policy analysis that looks at the role of energy management system aggregators and the end-users participating in subsidiary systems within Smart Grid programmers and technologies. In the implementation of aggregators for energy management systems, the objective is to understand the patterns, threats, obstacles and potential obstacles.

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

IoT Provisioning QoS based on Cloud and Fog Computing

The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.