DETECTION OF GRANULATION TISSUE FOR HEALING ASSESSMENT OF CHRONIC ULCERS

Wounds that fail to heal within an expected period develop into ulcers that cause severe pain and expose patients to limb amputation. Ulcer appearance changes gradually as ulcer tissues evolve throughout the healing process. Dermatologists assess the progression of ulcer healing based on visual inspection of ulcer tissues, which is inconsistent and subjective. The ability to measure objectively early stages of ulcer healing is important to improve clinical decisions and enhance the effectiveness of the treatment. Ulcer healing is indicated by the growth of granulation tissue that contains pigment haemoglobin that causes the red colour of the tissue. An approach based on utilising haemoglobin content as an image marker to detect regions of granulation tissue on ulcers surface using colour images of chronic ulcers is investigated in this study. The approach is utilised to develop a system that is able to detect regions of granulation tissue on ulcers surface using colour images of chronic ulcers

Artificial Intelligence in Commerce and Business to Deal with COVID-19 Pandemic

Many countries imposed a complete lockdown in response to the increase in the number of COVID-19 patients. Hence, it results in the devastating global economic crisis across the globe. The pandemic vigorously affecting the services sectors, supply chain, and global trade business with visible repercussions in the increase in the unemployment rate in various indus-tries. The rapid pace of AI and technologies are helpful to reshape their business during this hard time. The application of AI is resulting in a business and commerce world which is in-novative and smart. The technological implication of AI simplifying our lives and business complexities. This escalates the popularity of Artificial Intelligence (AI) and its methods like the Heuristics method, Support vector machines(SVM), Markov Decision Process,  Natural Language Processing (NLP), Fuzzy Logic, and Artificial Neural Network (ANN) to fathom the challenges arising due to the pandemic. Artificial Intelligence, Big Data, and Sensors are must in compliance with real-time data analysis, predictions for better decision making, in various sectors of the economy. The conglomerate, MSMEs, and start-up companies are em-phasizing more on the creation of digital enterprise leading by making their presence felt in various e-commerce platforms for the emergence of a new world. In this research paper, we also try to highlight some of the COVID-19 pandemic related issues that is basically affecting the commerce and business. This curren

Planning, operation, and design of market-based virtual power plant considering uncertainty

The power systems of today seem inseparable from clean energy sources such as wind turbines (WTs) and photovoltaics (PVs). However, due to their uncertain nature, operational challenges are expected when WT and PV energy is added to the electricity network. It is necessary to introduce new technologies to compensate for the intermittent nature of renewable energy sources (RESs). Therefore, rationally implementing a demand response (DR) program with energy storage systems (ESSs) in a virtual power plant (VPP) environment is recommended as a way forward to minimize the volatile nature of RESs and improve power system reliability. Our proposed approach aims to maximize social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). Our method assesses the impact of the DR program on SW maximization. Two scenarios are examined, one with and one without a DR program. Stochastic programming theory is used to address the optimization problem. The uncertain behavior of WTs, PVs, and load demand is modeled using a scenario-based approach. The correctness of the proposed approach is demonstrated on a 16-bus UK generic distribution system. Our results show that SW and active power dispatch capacity of WT, PV, and ESS are fairly increased using the proposed approach

Trends and Patterns in Artificial Intelligence Research for Oil and Gas Industry: A Bibliometric Review

Purpose: This paper aims to outline a broad-spectrum perspective of the structure of research in artificial intelligence (AI), in the oil and gas industry (OGI) based on bibliometric and distance-based visualisation of similarities (VOS) analysis.   Theoretical framework: The OGI has been one of the major contributors to the world economy. With the increasing energy demand, it has become necessary for the industry to adopt the latest technologies to enhance efficiency, reduce costs, and improve safety. One such technology is AI, which has the potential to revolutionise OGI.   Design/methodology/approach: The paper uses the data from Scopus online database as of April 2023. Based on “key-terms” search results, 251 valid documents were obtained for further analysis using VOS viewer software and Harzing’s Publish or Perish for citation metrics and analysis.   Findings: The finding shows that the Journal of Petroleum Science and Engineering is the field's most relevant journal, with 14 (5.58) published Articles. The People's Republic of China is the most productive country regarding AI research in the OGI. El-Sebakhy's (2009) article is the most cited article, with 113 citations and an average of 8.07 citations per year.   Research, Practical & Social implications: AI could transform OGI. Thus, adopting AI technologies can increase efficiency, reduce costs, and improve safety, also may increase productivity and economic benefits in AI research-intensive countries.   Originality/value: This study provides a comprehensive analysis of the existing AI research in the OGI, utilising bibliometric data and graphical networks

Student Empowerment / Preparedness: Key to Learning Effectiveness in Remote Teaching

Despite significant investment in resources for online delivery of courses, many universities struggled to ensure quality learning experience for their students during Covid-19. This research analyzes the factors that have a bearing on learning effectiveness. Results of the study clearly establish the criticality of student preparedness to learning effectiveness. We posit that empowering students to take charge of their learning through preparedness of all major stakeholders (student, faculty, and university) would positively affect the learning outcome for students. Effective learning experience is best achieved when institutions focus their efforts on empowering students to take control of their learning as opposed to simply investing in resources to deliver courses remotely

Optimization of the Urban Green Area in Erbil Territory for Sustainable Development

This research paper studies the status and condition of the green areas in the city of Erbil, for this purpose all green areas in the city (763 plots) and all population number according to 12 sectors are collected according to their locations and are analyzed spatially by GIS program (Moran I). The researchers have proved that distribution of green areas is random. Moreover, this distribution is not based on the urban planning basics and its criteria: green area per person (GAPP) and green area to the city ratio (GAR) also not based on the basics of urban planning for two criteria , GAPP is optimized from 9.3 to 14 and GAR optimized from 0.06 to 0.09 while the equilateral tringle adopted as optimum distribution for green area units GAU, for 12 sectors adopted combined standards together and the solution was the population density ratio must be 0.01 or less, to obtain criteria and this must preserved and adhered to the planning and laws and regulations strictly. This method can be applied to the study of the spatial distribution in order to compare it with the distribution of schools, health centers and other services or infrastructures