Early Recognition of Skin Malignancy in Images Based on Convolutional Networks by Using Dynamic System Model

Self Cite

Cancer is a noncommunicable chronic disease that indistinctly affects people of any nationality, race, ethnicity, age, or social class. Because of its unpredictability, receiving the diagnosis of this disease is almost always alarming for the patient. Breast cancer, especially among women, occupies a prominent position in this ranking. However, if diagnosed early, there is an excellent chance of a cure. In this sense, digital technologies have been advancing at an increasingly fast pace to support the early diagnosis of the disease. Clinical analysis of breast cancer is commonly performed using diagnostic imaging. One of the most used exams considered the main one for the early detection of this type of cancer is mammography. This exam allows the visualization of breast tissue from image screening using X-rays. In this sense, the use of computational techniques is essential to assist medical professionals in diagnosing this disease, thus making prevention and early diagnosis even more effective in the current context. The present work is limited to the use of digital technologies (image processing and artificial intelligence) that cooperate with the early diagnosis of breast cancer, which supports the medical professional to analyze images and be able to diagnose, from an early stage, the emergence of breast cancer of the disease, significantly increasing the chances of curing it. It can be gathered from this research how the discovery of X-rays and the growth in this sector combined with cutting-edge technology have benefitted in the early detection of the disease and even offered the cure of many cases.

“…The information that is obtained from these techniques helps the doctor's work. These are just some concepts and techniques for creating digital medical images [9].…”

Section: Review Of Literaturementioning

confidence: 99%

[Retracted] Early Diagnosis of Breast Cancer Using Image Processing Techniques

Younis¹,

Ali

Alhafidhb

et al. 2022

Self Cite

“…Some studies in underdeveloped countries indicate that due to diarrhea which is the main reason of poor sanitation, improper water use and poor sanitation lead to a total of 88% deaths [6,7]. In larger cities, such as Addis Ababa, there is an increasing demand for tap water as the community's demand for tap water is very high [6][7][8][9][10][11]. Respect and resilience have a great deal to do with the use of water.…”

Section: Introductionmentioning

confidence: 99%

Trend Analysis and Prediction on Water Consumption in Southwestern Ethiopia

Enbeyle

Hamad²,

Al-Obeidi³

et al. 2022

Self Cite

This study intensively examined the monthly water consumption forecasting performance using advanced time series (ARIMA) models. Thus, this study intends to identify the appropriate ARIMA models to best fit the water consumption data in Southwestern Ethiopia Tepi town and forecast water consumption effectively in the city. The data used for this study was the monthly water consumption in Tepi town from January 2016 to December 2021.The data were converted to returns to enhance their statistical properties and the returns were used to fit a mean equation. The monthly average water consumption in Tepi Town is 77227.8 meters cubic. Both original and transformed data show the trend of water consumption is increasing over time. Several ARIMA models were fitted to the data, and it emerged that the most adequate model for the data was ARIMA (1, 1, 1) based on the model selection criterion. The parameters for ARIMA models were estimated using the Ordinary Least Squares Estimation (OLS) method. The model was used to forecast the consumption for the next ten months and to advise Tepi town Water Company Limited in the city to meet the demand of the people. Conclusion. The consumption of water is increasing from December to September.

“…Data accuracy and availability at critical times are critical to the patients involved. Moreover, the performance of healthcare practitioners and staff is another key aspect evaluated in COVID-19 [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Software Measurement by Using Artificial Intelligence

Tariq

Awan

Alshudukhi

et al. 2022

Artificial intelligence (AI) is a subfield of computer science concerned with developing intelligent machines capable of performing tasks similar to those performed by humans. This human-created intelligence began more than 60 years ago. The goal of previous generations of applications was to demonstrate generic human-like behaviour. The goal has expanded with the advancement and increased compliance of this technology. It includes areas such as healthcare, gaming, and smart devices. The COVID-19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses and clinicians who have had to shift delivery modes quickly. In this study, we have conducted a systematic literature review (SLR) to provide an overview of the current state of the literature related to software measurement of healthcare using artificial intelligence. The study followed a secondary research strategy. The systematic literature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initial searches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the healthcare sector to bring efficiency to the systems. The research concluded that the healthcare setting crucially requires the implementation of software automation.