Artificial Intelligence in Deepfake Technologies Based on Supply Chain Strategy

It is highly impossible to differentiate between verified and fake media news and updates with modern emerging technology. The creation of deeper fake photos and videos which use artificial intelligence (AI), that shows someone saying and doing things that never did in reality, is one of the recent innovations that contribute to it. Concentrated depths will easily hit millions of citizens and affect adversely our culture, along with their accessibility and pace of social networking. This paper analyses a variety of widely accessible internet news stories, while literature on this subject is scarce, in order to explore the depths, and who creates them, how deep technological advantages and risks are, which example of deepfake occur and how the depths are to be gauged. We will use qualitative research methodology to gather the available information and based on the findings, in the end, we will conclude the discussions along with recommendations

Unsupervised Ranking of Numerical Observations based on Magnetic Properties and Correlation Coefficient

This paper addresses a novel unsupervised algorithm to rank numerical observations which is important in many applications in computer science, especially in information retrieval (IR). The proposed algorithm shows how correlation coefficients between attribute values and the concept of magnetic properties can be explored to rank multi-attribute numerical objects. One of the main reasons of using correlation coefficients between attribute values and the concept of magnetic properties is that they are easy to compute and interpret. Our proposed Unsupervised Ranking using Magnetic properties and Correlation coefficient (URMC) algorithm can use some or all the numerical attributes of objects and can also handle objects with missing attribute values. The proposed algorithm overcomes a major limitation of the state-of-the-art technique while achieving excellent results

Personalized blood pressure control by machine learning for remote patient monitoring

In the midst of a global health crisis, it is of utmost importance for healthcare technologies to possess the capability to regulate and monitor the physiological variables of patients remotely and automatically. The effective control of mean arterial pressure (MAP) in a closed-loop manner is particularly critical for individuals who are critically ill or in the process of recovering from surgical procedures. Within the framework of the present research, an adaptive closed-loop structure has been formulated with the objective of controlling a patient's MAP through governed administration of the medication sodium nitroprusside (SNP), to attain the desired MAP levels under varying conditions. The proposed closed-loop technique incorporates an intelligent controller known as the active disturbance rejection control (ADRC) with the intention of tracking the desired MAP value, alongside the utilization of continuous action policy gradient (CAPG) for the optimization of the controller's coefficients. Under the DRL strategy, an actor is responsible for generating policy requests, while a critic assesses the efficacy of the actor's policy directives. This approach uses gradient descent to train the weight values of both actor and critic networks, and it is dependent on the reward return linked to the MAP fault. Upon comparing the outcomes of the recommended structure with conventional models, numerical simulation results demonstrate the superiority of the proposed system in coping with varying working conditions, key-value fluctuations, and uncertainties, while effectively maintaining the desired mean arterial pressure and drug administration rate

Adaptive average arterial pressure control by multi-agent on-policy reinforcement learning

The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages. Following this, an observer is incorporated into the main controller to improve performance and stability by estimating states and disturbances. The controller parameters are optimized using MAOPRL in an adaptive manner, which involves the use of an actor-critic approach in an adaptive fashion. This approach enhances the adaptability of the controller by allowing for dynamic modifications to dosage and blood pressure control parameters. In the presence of disturbances or instabilities, the critic’s feedback aids the actor in adjusting actions to reduce their impact, utilizing a complementary strategy to tackle deficiencies in the primary controller. Lastly, various evaluations, including assessments under normal conditions, adaptability between patients, and stability evaluations against mixed disturbances, have been carried out to confirm the efficiency and viability of the proposed method

Transition to remote/hybrid learning during the COVID-19 pandemic among Saudi students of the College of Applied Medical Sciences: a cross-sectional study

BackgroundThe novel Coronavirus Disease 2019 (COVID-19) pandemic has presented unparalleled and unique stressors and challenges to the field of applied health sciences education. This study explored how the College of Applied Medical Sciences (COAMS) Saudi students perceive the transition to remote/ hybrid learning during the COVID-19 pandemic.MethodsA cross-sectional exploratory investigation was carried out during the months of February and March in the year 2023 among 196- COAMS Saudi students, using the 48-item previously developed and validated questionnaire, and with a non-probability convenient sampling technique. Descriptive statistics were generated for participants’ demographics, and for each questionnaire item and statistical analysis was performed using chi-square test.ResultsOut of the 283 undergraduates who have enrolled in COAMS, a total of 196 students have agreed to participate in the study with an overall response rate of 69.3%. Over 70% of COAMS students were satisfied and engaged in their on-site coursework. Nevertheless, questionnaire data indicates that their satisfaction and level of engagement diminished following the shift to remote learning. More than 62% of COAMS students were satisfied with their instructors’ instructional and assessment strategies during on-site coursework, but such perceptions have decreased with remote instruction. Hybrid learning can be beneficial and effective in improving the performance and learning experience of COAMS students. As compared to female students, COAMS male students were more satisfied with remote learning because it met their needs (p = 0.017).ConclusionRemote classrooms have lower attendance and interest than on-site classes. Despite lower satisfaction levels in online courses, hybrid learning was viewed favourably by COAMS students. Higher educational institutions should develop plans to increase student involvement, improve academic integrity, and assess the effect of the pandemic on undergraduate education on a regular basis. By incorporating these measures, educational institutions can enhance and support the remote learning experience for their students

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Background: Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. // Methods: We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung's disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. // Findings: We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung's disease) from 264 hospitals (89 in high-income countries, 166 in middle-income countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in low-income countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. // Interpretation: Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between low-income, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Saudi Arabia Corporate Firms are Hesitant to Embrace Artificial Intelligence as of 2020 Despite the Numerous Benefits

The modern world has experienced numerous advancements, particularly in the Artificial Intelligence (AI) sector which is slowly shaping the mode of business in Saudi Arabia. This research paper focuses on why there has been hesitation in the establishment of AI in Saudi Arabia firms despite the numerous potential advantages of AI. Saudi Arabia has, in recent times, undergone diversification in its economy. Its AI is experienced in the sectors such as robotics, industry, finance, and banking, especially in forecasting and improvement of business processes alongside provisions of solutions to complicated functionalities. However, AI is halfway implemented in the country due to various constraints explored in this publication. This research article examines companies hesitant to invest in AI and elaborates on its deployment in companies in Saudi Arabia to utilize such technologies in entire sectors. Moreover, our research also analyzes the total investment associated with AI to realize the desired change in the organizational operating activities. Our primary research involved over 2000 senior executives regarding the adoption of AI, the firm’s prospects for deployment, and its effects on the markets, governments, and persons. The results of this study showcase that the new AI generation is primarily based on the digitalization platform. Lastly, it is noted that AI deployment and acquisitions require a large number of funds hence forcing struggling firms to evade its adoption.</jats:p