Search results for: Sogand Barghi

Authors: Sogand Barghi

Abstract:

The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

Procedia PDF Downloads 31

Authors: Sogand Moshfeghi, Alireza Biglari

Abstract:

Diabetes is an endocrine disorder that is commonly treated with insulin. However, long-term usage of insulin and hypoglycemic chemical drugs can result in various side effects. Therefore, it is crucial to explore effective compounds with minimal side effects for diabetes treatment. This study aimed to compare the hypoglycemic effects of hydroalcoholic extracts derived from Silybum marianum, Camellia sinensis (green tea), and Urtica dioica plants. Male Wistar rats were allocated to 5 groups. Group 1 received normal Salin. Other groups were diabetic (induced by Streptozotocin 65 mg/kg Ip), group 2 received normal Salin (Ip, qod. 21 days). Group 3 received Silybum Marianum L, hydroalcoholic extract (100 mg/kg, ip.qod, 21 days). Group 4 received Camellia sinesis L, hydroalcoholic extract (100mg/kg,ip,qod,21 days), and group 5 received Urtica dioica L. hydroalcoholic extract (100mg/kg, ip,qod,21 days). Blood samples were collected at 14 and 21 days after the initial injection to evaluate the blood glucose levels. On the fourteenth day, the blood glucose levels for the diabetic groups were as follows: Group 2: 424.7±34.5, Group 3: 390.7±10.5, Group 4: 350.4±16.9, and Group 5: 340±20.5. On the 21st day, the respective blood glucose levels were: Group 2: 432±5.0, Group 3: 410.16±5.0, Group 4: 264.3±17.5, and Group 5: 270.7±24.5. Statistical analysis using the Tukey Anova test indicated that on the fourteenth day, both the green tea and Urtica groups exhibited significant hypoglycemic effects. Furthermore, on the 21st day, Urtica dioica extract demonstrated comparable effects to Camellia Sinensis extract, while Silybum Marianum extract did not significantly lower blood glucose levels compared to the diabetic group. In conclusion, the hydroalcoholic extracts from Camellia sinensis and Urtica dioica plants exhibited promising hypoglycemic effects in diabetic rats. These findings provide valuable insights into the potential use of natural plant extracts as alternative or complementary treatments for diabetes, warranting further investigation to harness their therapeutic benefit effectively.

Keywords: Camellia sinesis, glucose, Silybum marianum, Urtica dioica

Procedia PDF Downloads 34