Traditional Management Techniques Research Articles

Farmers’ traditional knowledge on seasonal frost management and their tree preferences in frost affected highlands of Amhara region, Ethiopia

Extreme environmental conditions, such as high temperature, frost, low humidity and desiccating winds, are the main causes for the failure of plantation forests in Ethiopia. Frost damage contributes to the low seedling survival and plantation success in frost affected highlands of Amhara region Ethiopia. Research outputs on farmers' traditional knowledge on frost management and technologies that curb frost related problems are limited in the country. This study was conducted to document the farmers’ traditional knowledge on frost management and their tree preference in frost affected highlands of Amhara region. Sinan, Guna-begemder and Meket districts were selected purposively in 2020. One representative sample Kebele was selected in each district with a total of 204 households selected with systematic random sampling technique for interview. In addition to interview focus group discussion was also conducted. Both descriptive and inferential statistics data analysis method were employed. In this study, frost was found to be an important limiting factor for plantation development in the study area. Frost occurs, mainly, from September to January. The local community applies traditional frost management techniques. The seasonality of frost occurrence and type of traditional frost management practices varied among the studied Kebeles. Eucalyptus globulus, Oldeania alpina, Rhamnus prinoides and Chamaecytisus palmensis were preferred trees species due to their frost resistant performance. The effectiveness of identified traditional frost management techniques needs to be investigated before they can be promoting and disseminating. Farmers should also be trained in proper techniques to protect planted seedlings from frost damage. Adjusting the timing of planting may also help to mitigate the effect of frost damage.

Soil solarisation delivers near zero levels of Fusarium pseudograminearum in cereal crown rot reference sites

The development of commercial varieties that are resistant or tolerant to crown rot caused by Fusarium species is an important goal for cereal breeding programs internationally. Ideally, this research requires experimental sites that are initially free from Fusarium in order to establish treatment plots that compare growth in the presence and absence of these soil- and stubble-borne pathogens. Specifically, the assessment of tolerance requires control plots free of disease to determine the reduction in crop yield in plots where the disease is present. The ability of soil solarisation to reduce the background Fusarium pseudograminearum level occurring at experimental sites in comparison to current stubble management techniques was investigated across three field trials at Wellcamp in Queensland. Stubble from a susceptible durum (Triticum turgidum ssp. durum) cultivar inoculated with F. pseudograminearum was incorporated by cultivation into the trial sites to establish a significant background level of inoculum prior to the application of all subsequent treatment plots. In these trials, solarisation over a period of twelve weeks reduced the presence of F. pseudograminearum to low detection levels when compared to the traditional crown rot management techniques of cultivation or growth of the non-host cover crops mungbean (Vigna radiata) and soybean (Glycine max). No negative effects of solarisation were observed on a subsequent crop of bread wheat (Triticum aestivum), with significantly higher yields observed in the solarised treatments. Solarisation has the potential to deliver near zero level crown rot reference sites for experimental purposes in one short application between cropping seasons.

AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand

The advent of artificial intelligence (AI) has ushered in a new era of efficiency and accuracy across various industries, with inventory management and demand forecasting being at the forefront of these advancements. Traditional inventory management techniques, often reliant on historical data and simple statistical models, fall short in addressing the dynamic and complex nature of contemporary markets (Chopra & Meindl, 2016). AI, with its advanced algorithms and machine learning capabilities, offers a transformative approach to these critical business functions. This paper explores the integration of AI technologies in optimizing inventory management and predicting customer demand. AI-enhanced inventory management involves the application of various AI technologies such as machine learning, natural language processing (NLP), computer vision, and robotics process automation (RPA) (Ivanov et al., 2017). Machine learning algorithms analyze vast amounts of historical data to identify patterns and trends, enabling more accurate predictions and adjustments in inventory levels. NLP processes unstructured data from sources like social media and customer reviews to provide deeper insights into market trends and customer preferences (Cambria & White, 2014). Computer vision technologies assist in real-time monitoring of inventory levels and identifying discrepancies through visual data, while RPA automates repetitive tasks like order processing and inventory tracking, thereby reducing human error and increasing efficiency (Aguirre & Rodriguez, 2017). This paper highlights significant improvements in forecast accuracy and inventory turnover rates achieved through AI implementation and discusses future implications for supply chain management.

Redefining Enterprise Data Management with AI-Powered Automation

In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.

Assessment of the Efficacy of Photobiomodulation (PBM) Therapy in Periodontal Treatment: A Longitudinal Study

ABSTRACT Objective: The intention of this longstanding study was to assess the effectiveness of photobiomodulation (PBM) therapy as a complementary periodontal therapy method. Methods: Fifty patients with periodontal disease were recruited and randomly randomized to receive either PBM therapy (n = 50) or traditional periodontal management (n = 50) as the control group. Periodontal parameters were measured at baseline and 3 and 6 months after management, including gingival index, probing depth, and clinical attachment level. Statistical analysis was applied. Results: At follow-up intervals, both the PBM management group and the control group showed significant improvements in periodontal markers. The PBM therapy group confirmed a higher degree of improvement than the control group, with statistically significant variations between the groups at 3 and 6 months post management (P < 0.05). Conclusion: In summary, PBM therapy outperforms traditional management techniques in terms of improving periodontal metrics. The incorporation of PBM therapy as an additional therapeutic method for the management of periodontal disease into clinical practice is supported by these findings. To improve management procedures and clarify the longstanding impacts of PBM therapy on periodontal tissue regeneration and clinical results, more investigation is necessary.

Effectiveness of Preoperative Alpha Wave Entrainment in Pediatric Dental Patients: A Randomized Controlled Trial.

Background Pediatric dental anxiety is a significant barrier to effective dental care, necessitating non-pharmacological interventions. Alpha wave entrainment has shown promise in adult studies for reducing procedural anxiety and pain perception, but its effectiveness in pediatric dental settings remains underexplored. Objective This study aims to evaluate the effectiveness of preoperative alpha wave entrainment in alleviating anxiety in gender-specific participants to the interventions. Methods We conducted a randomized controlled trial involving 252 pediatric patients (aged 7-12) with cooperative dispositions. Participants were randomly assigned to either an experimental group receiving alpha wave entrainment or a control group receiving conventional behavior management techniques. The experimental interventioninvolved 10-minute sessions of binaural beats with visualstimulation designed to induce alpha-wave synchronization. Anxiety levels were assessed using physiological measures (heart rate and blood pressure), both pre- and post-interventions. Results The intervention group demonstrated a significant reduction in heart rate and systolic blood pressure post-intervention compared to the control group. These changes indicate a decrease in anxiety levels, with no significant gender differences in the response to the intervention. Conclusion Alpha wave entrainment effectively reduces dental anxiety in pediatric patients, with similar efficacy observed across genders. This study supports the incorporation of alpha wave entrainment into pediatric dental practices as a viable alternative to traditional anxiety management techniques.

Case Report: Uterine artery embolization after dysfunctional uterine bleeding due to incomplete abortion in female

Dysfunctional uterine bleeding (DUB), a serious clinical challenge caused by incomplete abortion in female, calls for efficient and minimally invasive therapies to treat symptoms and protect reproductive health. Uterine artery embolization (UAE) has been discovered as a viable therapeutic strategy to treat DUB following an incomplete abortion. The existing information and clinical experience of UAE for this particular indication were thoroughly reviewed. A female’s quality of life may be negatively affected by incomplete abortion, which is defined as residual fetal tissue inside the uterine cavity. This frequently results in heavy and continuous uterine bleeding. Medical procedures and traditional management techniques such as dilatation and curettage (D&C) do not always produce the desired effects or might have inherent dangers. UAE, a less invasive technique first developed for treating uterine fibroids, has shown promise as a substitute solution for DUB caused by an incomplete abortion.

Monitoring and Controlling of Greenhouse

Abstract: The potential for revolutionizing crop production practices has drawn significant attention to the monitoring and management of greenhouse settings. With the help of greenhouses, plants may grow in a controlled environment with the right amount of light, heat, humidity, and soil moisture. However, manual monitoring and intervention—which may be timeconsuming, laLbor-intensive, and prone to human error are frequently used in traditional greenhouse management techniques. The design, implementation, and assessment of a greenhouse monitoring and controlling system built around the ESP32 controller are proposed in this research paper. In order to gather environmental data—such as temperature, humidity, soil moisture, and light intensity—that is essential for determining plant health and maximizing growth conditions, the system incorporates sensors. Farmers may remotely get real-time insights into their greenhouse operations by transmitting the acquired data to a cloud platform or central monitoring station using the ESP32's networking features. the incorporation of ESP32 controllers into greenhouse management is a noteworthy technical development that has the potential to completely transform current agricultural methods. ESP32-based systems give farmers a strong tool for maximizing agricultural productivity while reducing resource inputs and environmental effects by providing real-time monitoring and precise control of environmental conditions

THE ROLE OF DIGITIZATION IN MANAGEMENT AND STRATEGIC DECISION-MAKING IN MODERN MANAGEMENT

This research aims to assess the impact of digitization on management efficiency and strategic decision-making, a pivotal aspect in the contemporary business world. By integrating digital technologies with traditional mathematical models and analyzing survey data from 250 management professionals, the study provides a comprehensive understanding of the multifaceted effects of digitization.The principal outcomes from the mathematical analyses, including Marginal Cost Analysis, Break-Even Analysis, and Linear Programming, indicate substantial improvements in operational efficiency following digitization. Specifically, Marginal Cost Analysis shows a significant decrease in the cost of production, and Break-Even Analysis reveals a reduction in the break-even point by approximately 15%, underscoring enhanced cost-effectiveness. Linear Programming results demonstrate a 20% improvement in resource allocation efficiency. The survey analysis complements these findings, revealing a positive perception of digitization in enhancing managerial adaptability, efficiency, and decision-making effectiveness, with average rating improvements of 0.3, 0.4, and 0.5 points, respectively, on a 5-point scale. Regression analysis further highlights the positive influence of technological affinity on decision-making effectiveness. However, ANOVA results suggest variability in the impact of digitization across different industry sectors, necessitating sector-specific digital strategies.Digitization significantly elevates management efficiency and strategic decision-making capabilities. The integration of digital tools with traditional management techniques results in more informed, efficient, and strategic business decisions. However, the varied impact across sectors and the rapid pace of technological advancement calls for continuous adaptation and sector-specific strategies. These findings provide vital insights for businesses navigating the digital era, emphasizing the strategic integration of digital tools in management practices for sustained success and competitiveness.

Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning.

Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model's performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

Toward a dynamic frame-based ontology of legal terminology

In the study of special languages and translation, the legal field is often insulated from other domains. This is primarily due to the extreme system dependence of the terminology of law, which results from a lack of a common legal system of reference throughout the world. The abstract nature of this human-made field and its dynamicity in view of the continuously evolving case law and constant changes in legislation make it difficult to illustrate its complex ontology through traditional terminology management techniques. Therefore, this paper argues for an interdisciplinary approach to constructing the ontology of legal concepts based on structural constituents from frame semantics and comparative law. Frames allowing for the representation of interconnected knowledge segments evoked by legal concepts and the distinction between micro- and macro-dimensions in legal comparison research make it possible to capture the complex ontology of legal terminology evoked in a specific point in time and a determined legal context. The ontological knowledge structure will be exemplified by terms from German social, commercial, employment, and tax law.

Artificial Intelligence Models for Predicting Budget Expenditures

This study develops and tests a machine learning (ML)-based cost forecasting model against traditional earned value management (EVM) techniques. Utilizing Python for ML implementation, the research applies algorithms to a dataset of completed projects globally, evaluating their performance with metrics like mean absolute percentage error (MAPE) and percentage error (PE). The results confirmed that ML give more accurate results than the traditional methods. Thus, the initial rate showing that XGBoost is more accurate than the traditional method using Index-2 is 88%. In 23 of 25 randomly selected projects, this algorithm was more accurate. At the middle stage, the same frequency is 92.6%; later stage, the selected criterion further confirms that the ML algorithm is more accurate than the traditional method, accounting for 75% of 21 projects out of 28. By introducing ML into project management forecasting, managers could spend less time on the technical tasks in their projects. Despite its effectiveness, the study's scope is limited by a small sample size of 110 projects and the testing of only three algorithms. Future research is suggested to expand the dataset and explore additional algorithms, including neural networks and tree-based methods, to enhance forecasting precision.

Virtual Reality Relaxation for Stress in Young Adults: A Remotely Delivered Pilot Study in Participants’ Homes

AbstractHigh stress levels experienced by young adults were exacerbated by COVID-19 and traditional stress management techniques can be challenging. Virtual reality (VR) relaxation appears promising and is accessible remotely; however, research on young adults is limited, especially in naturalistic settings. This remotely delivered and controlled mixed-methods study investigated feasibility and acceptability of VR relaxation for young adults (N = 23, mean age = 22.96) at home during the pandemic. VR participants (N = 11) were asked to complete daily VR relaxation sessions (scenes of virtual beaches, mountains, the sea, and more) for a 14-day intervention period, alongside visual analogue scales (VAS) of psychological well-being pre- and post-VR sessions. Post-intervention, VR participants completed semi-structured interviews. Control participants (N = 12) received no intervention. Both groups completed outcome measures of psychological well-being pre- and post-intervention periods to evaluate feasibility of remote data collection. VAS results indicated acceptability of daily VR, with significant short-term increases in relaxation, happiness, and connectedness to nature, and decreases in stress, anxiety, and sadness within VR participants. VR usage and 100% completion of outcome measures indicated feasibility of VR relaxation and remote data collection. Qualitative findings suggest participants found the VR relaxing, but interest diminished over time and technical issues hindered user experience. This suggests a more informal, less prescriptive frequency of VR use may be more appealing for young adults, although adherence may relate to technical issues. Greater interactivity and remote technical assistance within VR could increase engagement. Future research should investigate optimal frequency of use, longer-term impact, and technical issues with a larger sample.

Traditional Land and Agriculture Management Techniques in Siku and Sibia Watershed, East Sinag District, Arunachal Pradesh

Traditional Land and Agriculture Management Techniques in Siku and Sibia Watershed, East Sinag District, Arunachal Pradesh

The living religious heritage of displaced communities: conservation problematics and use-related challenges

PurposePlaces of worship have historically been maintained using traditional building management techniques, including regular monitoring, upkeep and maintenance provided by their religious communities. This paper examines the conservation issues arising after the forced displacement of the traditional custodians, which is a significant concern in conflict-ridden environments.Design/methodology/approachAs a unique example of a long-term conflict, the divided Cyprus provides this research with illustrative cases to derive the data. The research employs content analysis of official documents, physical observations and interviews with conservation professionals.FindingsThis research demonstrates the human and environmental factors impacting the conservation of the material fabric and the use-related challenges stemming from the intangible significance of the religious legacy belonging to displaced communities. It highlights the urgency to formulate more effective mechanisms and regulatory frameworks to address vulnerability issues promptly.Originality/valuePreservation problems on religious heritage buildings arising from the loss of traditional custodians after conflicts are an under-researched area in conservation literature. Drawing on research that was conducted several decades after the displacement of Cypriot communities, this paper reveals new insights into the magnitude of the conservation problems and the use-related complexities that need to be addressed to formulate mutually acceptable solutions for a sustainable future.

Ultrasound-guided erector spinae plane block for perioperative analgesia in patients undergoing laparoscopic nephrectomies surgery: a randomized controlled trial

BackgroundLaparoscopic nephrectomy is a commonly utilized surgical approach for the management of renal cancer. Despite its widespread acceptance, postoperative pain management remains a significant challenge for many patients undergoing this procedure. Traditional pain management techniques, including opioid and nonsteroidal anti-inflammatory drug administration, may not provide adequate pain relief and may result in adverse effects. In recent years, erector spinae plane block (ESPB) has emerged as a promising regional anesthesia technique due to its simplicity, safety, and potential efficacy in reducing postoperative pain. ESPB has demonstrated effectiveness in reducing postoperative pain in various surgical procedures. However, the efficacy of ESPB in laparoscopic nephrectomy for renal cancer has not been extensively studied. As such, further investigation is necessary to determine the potential benefits of ESPB in this context. The addition of adjuvants such as dexmedetomidine and dexamethasone to nerve blocks has been shown to improve both the duration and quality of the block. Multiple studies have demonstrated the safety and efficacy of these adjuvants in reducing postoperative pain and opioid consumption and improving patient satisfaction. The use of dexmedetomidine and dexamethasone as adjuvants for nerve blocks represents a promising approach for enhancing regional anesthesia and analgesia. In light of these findings, we have incorporated dexmedetomidine and dexamethasone into our nerve block protocol.MethodsThis study is a randomized controlled trial conducted at a single center, with 50 participants being randomized in a 1:1 ratio to either the ESPB group or the control group. The trial aims to investigate the efficacy of ESPB in patients diagnosed with kidney cancer who are scheduled for laparoscopic nephrectomy. The primary outcome measure is the total consumption of intraoperative sufentanil. Secondary outcomes include the VAS score at rest and during coughing at 1 h, 6 h, 12 h, 24 h, and 48 h after surgery; total intraoperative remifentanil consumption; the number of times rescue analgesia is required; and the incidence of nausea and vomiting in the first 24 h after surgery. This study is registered for a duration of 1 year and is being conducted in China.DiscussionThe objective of our study is to evaluate the potential benefits of erector spinae plane block (ESPB) in patients undergoing laparoscopic nephrectomy, with a focus on the impact of dexmedetomidine and dexamethasone as adjuvants on the quality and duration of the block, as well as postoperative pain and opioid consumption. By investigating the effects of these adjuvants in the context of ESPB, we hope to contribute to the growing body of literature on the use of adjuvants in nerve blocks and provide further insight into the potential benefits of this approach for improving patient outcomes following laparoscopic nephrectomy. This trial was approved by the Ethics Committee of the Second Affiliated Hospital of Army Medical University.Trial registrationChina Clinical Trial Register ChiCTR2300068578. Registered on 20 February 2023.

Fungal-mediated synthesis of silver nanoparticles: a novel strategy for plant disease management.

Various traditional management techniques are employed to control plant diseases caused by bacteria and fungi. However, due to their drawbacks and adverse environmental effects, there is a shift toward employing more eco-friendly methods that are less harmful to the environment and human health. The main aim of the study was to biosynthesize silver Nanoparticles (AgNPs) from Rhizoctonia solani and Cladosporium cladosporioides using a green approach and to test the antimycotic activity of these biosynthesized AgNPs against a variety of pathogenic fungi. The characterization of samples was done by using UV-visible spectroscopy, SEM (scanning electron microscopy), FTIR (fourier transmission infrared spectroscopy), and XRD (X-ray diffractometry). During the study, the presence of strong plasmon absorbance bands at 420 and 450 nm confirmed the AgNPs biosynthesis by the fungi Rhizoctonia solani and Cladosporium cladosporioides. The biosynthesized AgNPs were 80-100 nm in size, asymmetrical in shape and became spherical to sub-spherical when aggregated. Assessment of the antifungal activity of the silver nanoparticles against various plant pathogenic fungi was carried out by agar well diffusion assay. Different concentration of AgNPs, 5 mg/mL 10 mg/mL and 15 mg/mL were tested to know the inhibitory effect of fungal plant pathogens viz. Aspergillus flavus, Penicillium citrinum, Fusarium oxysporum, Fusarium metavorans, and Aspergillus aflatoxiformans. However, 15 mg/mL concentration of the AgNPs showed excellent inhibitory activity against all tested fungal pathogens. Thus, the obtained results clearly suggest that silver nanoparticles may have important applications in controlling various plant diseases caused by fungi.

Leveraging lean 4.0 technologies in healthcare: An exploration of its applications

Lean 4.0, a fusion of lean manufacturing and digital technologies, is a powerful tool for implementing Industry 4.0. Its widespread use across sectors is driven by its ability to eliminate waste, reduce errors, boost revenue, and enhance customer care and efficiency. In the healthcare sector, this fourth lean revolution is particularly beneficial, as it helps to curb wastage and enhance efficiency, leading to more effective outcomes. Lean 4.0, a globally recognized best practice for industrial organisations, has proven its worth in enhancing performance and competitiveness. Its success has led to its adoption not only in manufacturing but also in non-manufacturing sectors such as Healthcare, finance, telecommunications, construction, and logistics. Its ease of use and effectiveness make it a superior alternative to traditional medical waste management techniques. Relevant papers on Lean 4.0 for Healthcare in the current literature are identified and studied to evaluate the impact of Lean 4.0 principles in Healthcare. This review-based research studies Lean 4.0 technologies and their needs in Healthcare. It discusses sustainable support for the healthcare sphere through Lean 4.0. Paper briefs several modalities of Lean 4.0 for the healthcare domain. Finally, the paper identifies and discusses the significant applications of Lean 4.0 in Healthcare. Lean 4.0 is essential to lowering errors that lead to medical mistakes since this business deals with life-or-death situations. Healthcare institutions need to reduce their overall waste reduction. With Lean 4.0, staff members working in quality management, clinical operations, consulting, and patient experience can progress into leadership roles in the healthcare sector. Lean 4.0 technologies can reduce waste production, including employing reusable medical equipment or cutting down on packaging waste. The possible difficulty of integrating digital technologies with healthcare systems and practices is Lean 4.0's limitations for the healthcare industry. Healthcare institutions can use refillable syringes and other disposable items to reduce waste rather than disposable plastic syringes. In future, healthcare institutions may save costs and lessen their environmental effect by minimising trash by using lean 4.0 technologies.

Coinfection prevalence of gastrointestinal parasites and ticks of small ruminants in Bui and Donga-Mantung Divisions, North West Region of Cameroon

Background: This study was conducted to determine the coinfection prevalence of gastrointestinal parasites and tick infestation of small ruminants in Bui and Donga-Mantung Divisions. Methods: 704 animals were physically examined for tick infestation and stool samples collected for the detection of gastrointestinal parasites, of which 383 were goats and 321 sheep. Results: Out of this number of animals examined, 378 were infected with one or more gastrointestinal parasite and ticks, giving an overall prevalence of 53.7% with the highest prevalence recorded in goats (P>0.05). The most common mixed infections recorded were Haemonchus sp/Eimeria sp/ticks, strongyloides sp/ trichostrongylus sp/ticks. Generally, the overall prevalence of these parasites and tick infestation among the different age groups showed that, adults had the highest overall prevalence (64.4%) while the young recorded the lowest prevalence (33.2%). It was however observed that females were the most infected (57.4%) compared to males (49.7%) (P>0.05). Locality based prevalence revealed that Bui Division was the most infected, with goats recording the highest prevalence (55.4%) while in Donga Mantung Division, the highest prevalence was recorded in sheep (50.4%) (P>0.05). Conclusion: This study provides an important step to reduce animal infection and infestation and minimize economic losses in small ruminants. it also provides information that will help farmers of these areas to use strategic treatment methods and medicinal plants to reduce parasite infestations on the animals and also to practice the right traditional management techniques.

Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies

Artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made. It has the ability to improve accuracy and efficiency while also lowering costs and providing fresh insights into financial markets and investments. This review will look at some of the important areas of financial management research that AI has influenced. One of the most notable effects of AI on financial management research has been in risk management. AI algorithms, with their ability to analyze vast amounts of data, can detect patterns and anomalies that humans may not notice right away and have had a significant impact on financial forecasting and prediction, generating predictions about future market movements and investment opportunities by analyzing historical data, market trends, and other relevant factors. In conclusion, artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made, with significant potential to disrupt traditional financial management techniques. AI has the ability to increase accuracy and efficiency, cut costs, and provide fresh insights into financial markets and investments. As AI technology advances, its impact on financial management studies is likely to rise.