Role Of Artificial Intelligence Research Articles

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.

Abstract PO1-26-01: Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer

Abstract Background: Patient-derived xenografts (PDXs) are pivotal in cancer research. Despite histopathological insights into factors driving PDX success, the role of artificial intelligence (AI) in predicting PDX engraftment remains unexplored. We aimed to bridge this gap by integrating clinicopathological data and AI-based morphometric analysis to predict PDX success in breast cancer. Methods: PDXs were generated from tumor tissues derived from breast cancer patients who had undergone surgical intervention. Clinicopathological information including subtypes, pathological diagnosis, modified Bloom-Richardson system histologic grades, treatment with neoadjuvant chemotherapy (NAC), Miller Payne grade, residual caner burden score, invasive tumor size, lymphovascular invasion status, AJCC 8th T and N stages and the percentage of tumor infiltrating lymphocytes were collected and analyzed. For the image analysis component, whole-slide images (WSIs) of hematoxylin and eosin–stained tissue samples from 64 surgically resected breast cancer patients were used as a training set for an AI model under the supervision of 2 pathologists to extract morphometric features. The model transformed image tiles into patches of morphologically similar patterns, and categorized them into adipose tissue, background, necrosis, invasive carcinoma, carcinoma in situ, stroma, and terminal ductal lobular unit. This trained model was subsequently applied to the WSIs employed in the establishment of PDXs. The classified patches and their relative ratios within the invasive tumor boundary were compiled. The consolidated data from clinicopathological and image analyses were subjected to logistic regression to discern correlates of successful PDX engraftment. Results: Out of the 311 patient tumor samples used for generating PDXs, (131 post-chemotherapy, 180 chemo-naïve), 47 PDXs were successfully established (15.1%). Logistic regression revealed several factors for successful engraftment including NAC treatment with an odds ratio of 6.71 (28.2% vs 5.6%, 95% CI: 2.53 - 17.80, p < 0.001), higher histologic grades (25.3% vs 2.2%, OR = 5.80, 95% CI: 1.49 - 22.63, p = 0.01), triple negative breast cancer compared to hormone receptor-positive cancers (32.0% vs 3.3%, OR = 10.99, 95% CI: 1.26 - 95.89, p = 0.03), and tumors of larger size (OR = 1.34, 95% CI: 1.02 - 1.76, p = 0.03). Interestingly, the percentage of specific tissue patch types within tumor did not significantly impact the likelihood of successful PDX engraftment. However, in our logistic regression analysis based solely on morphometric features, presence of necrosis within the tumor notably enhanced PDX establishment. Specifically, each percent increase in necrosis within tumor boosted the odds of successful PDX creation by 0.01% (OR = 1.0001, 95% CI: 1.00003 - 1.00024, p = 0.01). Conclusions: PDXs are often successfully established from clinically aggressive breast cancers, particularly those with NAC treatment, higher histologic grades, TNBC subtype, and larger tumor size. While morphometric features contribute to the prediction of PDX engraftment success, their importance is surpassed by these clinicopathological factors. However, presence of necrosis emerged as a key morphometric predictor of successful PDX engraftment. Keywords: breast cancer; patient-derived xenograft (PDX); Breast Cancer Morphometrics; Cancer Predictive Modeling. Citation Format: Jongwon Lee, GeonHee Lee, Gyungyub Gong, Hee Jin Lee. Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-01.

Abstract PO2-28-04: Pregnancy-associated relapse of triple-negative breast cancer

Abstract Introduction: Pregnancy Triple-negative breast cancer(TNMC) is the absence of ER PR and HER2 receptor expression. It constitutes about 15-20% of all breast cancers and is known to be the aggressive form with a high relapse rate (1). Interestingly, it usually affects women aged < 40 and has a low survival rate in African-American females. This case is a typical presentation of a 38-year-old young woman with PMH of T2NO right triple-negative breast cancer diagnosed in 2017 s/p right mastectomy with sentinel LN biopsy, received immunotherapy and chemotherapy, has been cancer-free for two years, and developed metastatic cancer during her pregnancy. In this case, we understand the diagnostic and therapeutic challenges of TNBC during pregnancy. Case: A 38-year-old African American woman, G0, T2NO triple negative invasive ductal carcinoma, underwent a right breast mastectomy and sentinel LN biopsy. She received treatment with (Anti-PD-L1 Antibody) concomitant with weekly paclitaxel and Doxorubicin/Cyclophosphamide (AC) chemotherapy. However, she had a residual 4cm tumor for which she was treated with adjuvant therapy with capecitabine for six months. A repeat Mammogram in 2019 showed no evidence of malignancy. In 2021, the patient was G2P2L1, admitted to the hospital after having a seizure. CT reported cystic/necrotic left parietal lesion mass effect with 8mm right-sided midline shift with metastasis to lungs and abdominal wall. She underwent L parietal craniectomy with resection of the tumor. At 32 weeks of gestation , chemotherapy with carboplatin and paclitaxel was given after her C-section and Gamma knife radiosurgery for metastatic lesions. She developed super refractory seizures, was induced into a coma with therapeutically anesthetic agents, and passed eventually. Discussion: Little literature exists on pregnancy-associated relapse of triple-negative breast cancer and its deterioration. Our patient was in remission for two years and was diagnosed with metastatic cancer during her second pregnancy. John et al., (2018) discussed an interesting association between breastfeeding, parity, and occurrence of TNBC, that there is a two-fold increased risk of developing TNBC with high parity(3 full-term pregnancies) and short-term breastfeeding (< 12 months) (2). Studies have also shown that cancers during or postpartum pregnancy are aggressive with more tumor burden and metastasize to the lungs, liver, and brain, which is consistent with our patient. (3) Conclusion: Pregnancy-associated breast cancer is diagnostically and therapeutically challenging during or in the postpartum period and with a high propensity of the triple-negative type. It requires a multidisciplinary team approach, awareness in the African American population, clinical suspicion of breast mass during pregnancy warrants imaging, and prolonged breastfeeding postpartum. Diagnosis is usually delayed due to pregnancy, and more research is needed on the diagnostic modalities during pregnancy for early treatment initiation. References Almansour NM. Triple-Negative Breast Cancer: A Brief Review About Epidemiology, Risk Factors, Signaling Pathways, Treatment and Role of Artificial Intelligence. Front Mol Biosci. 2022 Jan 25;9:836417. doi: 10.3389/fmolb.2022.836417. PMID: 35145999; PMCID: PMC8824427. John EM, Hines LM, Phipps AI, et al. Reproductive history, breast-feeding and risk of triple-negative breast cancer: The Breast Cancer Etiology in Minorities (BEM) study. International Journal of Cancer. 2018;142(11):2273. doi:10.1002/ijc.31258 Amant F, Deckers S, Van Calsteren K, Loibl S, Halaska M, Brepoels L, et al. Breast cancer in pregnancy: recommendations of an international consensus meeting. Eur J Cancer (2010) 46(18):3158–68. 10.1016/j.ejca.2010.09.010 Citation Format: Vaidarshi Abbagoni. Pregnancy-associated relapse of triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-04.

AI TOOLS FOR NETWORKING: A SURVEY

Over the past few decades, there has been a significant development in the technology industry. Major technological advances like the Internet and automation are now the centre of attention. Technology that can perform significant tasks that were previously thought to be impossible has already been invented by humans. One of the most cutting-edge technologies that enable humans to copy and carry out a range of jobs is artificial intelligence. This paper focuses on the role of artificial intelligence in computer networks. One must first understand the ideas related to artificial intelligence and networks in order to properly understand what artificial intelligence is and how it functions in computer networks. AI networking has the potential to significantly increase productivity by upending established traditional networking practices.Artificial intelligence networking is an evolution of AIOps (AI for IT operations) that focuses on ongoing management, maintenance and optimization of a network. It combines AI with networking infrastructure to automate and optimize IT operations. Where AIOps has a broader focus on the information and operations (I&O) infrastructure level, AI networking is specific to the networking domain (data center switching, wired, wireless, LAN, WAN, SD-WAN, multicloud).

AI Tools for Networking: A Survey

Over the past few decades, there has been a significant development in the technology industry. Major technological advances like the Internet and automation are now the centre of attention. Technology that can perform significant tasks that were previously thought to be impossible has already been invented by humans. One of the most cutting-edge technologies that enable humans to copy and carry out a range of jobs is artificial intelligence. This paper focuses on the role of artificial intelligence in computer networks. One must first understand the ideas related to artificial intelligence and networks in order to properly understand what artificial intelligence is and how it functions in computer networks. AI networking has the potential to significantly increase productivity by upending established traditional networking practices.Artificial intelligence networking is an evolution of AIOps (AI for IT operations) that focuses on ongoing management, maintenance and optimization of a network. It combines AI with networking infrastructure to automate and optimize IT operations. Where AIOps has a broader focus on the information and operations (I&O) infrastructure level, AI networking is specific to the networking domain (data center switching, wired, wireless, LAN, WAN, SD-WAN, multicloud).

Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study.

The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system. A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05). A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system. This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.

The Role of Artificial Intelligence in Personalized Marketing Strategies of AMAZON

In today's digitally driven marketplace, personalized marketing has emerged as a pivotal strategy for businesses aiming to engage customers on a deeper level and foster long-term relationships. With the advent of Artificial Intelligence (AI), personalized marketing has reached unprecedented levels of customization and effectiveness. This master's thesis delves into the intricate relationship between AI and personalized marketing strategies, focusing on how AI-powered techniques can revolutionize customer engagement and satisfaction. The research explores various facets of personalized marketing, including data collection, analysis, and implementation of tailored campaigns. Through a comprehensive literature review, this thesis examines the theoretical underpinnings of personalized marketing and the evolution of AI technologies in this domain. Moreover, empirical studies and case analyses are conducted to illustrate real-world applications and outcomes of AI-driven personalized marketing strategies across diverse industries. Key themes investigated include the role of machine learning algorithms in deciphering consumer behavior patterns, the ethical considerations surrounding data privacy and consent, and the implications of AI-generated content on brand-consumer interactions. Additionally, the study investigates the impact of personalized recommendations, dynamic pricing, and predictive analytics on enhancing customer satisfaction and loyalty. Furthermore, this research highlights the challenges and opportunities associated with integrating AI into personalized marketing initiatives, such as algorithm bias, algorithmic transparency, and the need for continuous adaptation to evolving consumer preferences. Insights gathered from interviews with industry experts and surveys of consumers contribute to a comprehensive understanding of the practical implications and future directions of AI-enabled personalized marketing

The role of artificial intelligence in optimising patient recruitment for breast cancer clinical trials

The role of artificial intelligence in optimising patient recruitment for breast cancer clinical trials

Role of artificial intelligence in education: A conceptual review

The increasing availability of Artificial Intelligence and related tools and technologies has made it possible to use AI in various domains of life. The field of education is also witnessing an increasing use of AI; however, its scope and related challenges remain unclear. In the current paper, we, by using previous empirical and theoretical studies, provided an overview of the use of AI in the education field, its related challenges, and future opportunities. Accordingly, we noted that AI is being used in the Education field as evidenced by global initiatives such as UNESCO. We also noted that AI applications in the education field include enabling the preparation of content, assignments, automated grading, and assistance to students. The benefits of AI in education include greater flexibility in terms of time and space and a changing role of the tutor as a facilitator. We also discussed key challenges including ethical issues such as data privacy, lack of inclusion and equity for students of all backgrounds, and lack of human touch. Based on these themes we concluded that AI's role in education will increase in the future, but the challenges also need to be addressed to fully unlock the benefits of AI in the education field.

Implementing AI in banking customer service: A review of current trends and future applications

In the dynamic realm of banking, the advent of Artificial Intelligence (AI) has catalyzed a transformative shift, redefining the paradigms of customer service and operational efficiency. This scholarly paper delves into the intricate interplay between AI and banking, aiming to elucidate the multifarious impacts of AI integration within this sector. Anchored in a robust thematic analysis and an exhaustive literature review, the study meticulously navigates through the evolution, current applications and prospective future of AI in banking, with a particular focus on enhancing customer experience and addressing the operational challenges. The methodology adopted herein, comprising both qualitative and quantitative analyses, facilitates a comprehensive exploration of AI's role in banking, unveiling its potential to revolutionize service delivery, risk management and customer engagement. The findings from this investigation underscore the significant enhancements in customer service metrics attributed to AI, alongside the emergence of personalized banking experiences. Furthermore, the study illuminates the strategic implications for banks adopting AI technologies, highlighting the necessity of navigating ethical and privacy concerns meticulously. The conclusions drawn advocate for a balanced approach towards AI adoption, emphasizing the imperative of ongoing research and development, coupled with ethical considerations, to harness AI's full potential responsibly. In essence, this paper offers a scholarly synthesis of AI's transformative impact on banking, providing a roadmap for future explorations in this domain. It lays down the gauntlet for banks to leverage AI's potential judiciously, guiding the banking sector towards a future where technology and human ingenuity converge to redefine banking's essence. The recommendations proffered herein underscore the importance of ethical governance and continuous innovation in navigating the AI-driven future of banking.

Enhancing large-span structure design and maintenance through the synergy of CAD, BIM, and IoT-AI technologies

The integration of Computer-Aided Design (CAD), Building Information Modeling (BIM), and Internet of Things (IoT) with Artificial Intelligence (AI) represents a transformative shift in the architectural and engineering practices for large-span structures. This paper delves into the quantitative and qualitative enhancements brought about by these technologies, focusing on their pivotal roles in design precision, structural analysis, project management, and maintenance strategies. CAD's evolution from simple drafting to complex 3D modeling has significantly reduced design time and errors, while its integration with structural analysis software has improved load distribution accuracy and material cost efficiency. BIM's contribution to design, construction, and sustainability emphasizes optimized resource allocation and energy-efficient practices. Furthermore, IoT and AI's role in real-time monitoring and predictive maintenance underpins a proactive approach to structural health, ensuring longevity and safety. Through statistical analysis, mathematical modeling, and case studies, this paper highlights the synergistic impact of these technologies on reducing project timelines, enhancing collaboration, and fostering innovation in large-span structure engineering. The findings advocate for a multidisciplinary approach, combining engineering expertise with advanced computational tools to achieve superior outcomes in the construction and maintenance of large-span structures.

The role of artificial intelligence in drying and biomass valorization in the field of phytoremediation of contaminated soils

The role of artificial intelligence in drying and biomass valorization in the field of phytoremediation of contaminated soils

Foreword

As technology advances, there are growing arguments about artificial intelligence, which is still controversial, with both welcome and pessimistic views regarding its role in the future of human life. What was revealed about the development of artificial intelligence is considered very simple in the eyes of specialists and observers, especially in areas that have not been explored, and this constitutes a source of fear for many. The vast leaps witnessed in recent years in the field of artificial intelligence have become a source of anxiety for most people, especially those working in institutions and industrial facilities, even though this anxiety stems from losing their jobs and replacing them with robots. This concern came from statements that the next two decades will witness the introduction of vast quantities of robots to the market. OpenAI was initially established in 2015 by Sam Altman, Elon Musk, and others to advance digital intelligence. On November 30, 2022, OpenAI released ChatGPT (Generative Pre-trained Transformer. ChatGPT uses a large language model to provide users with quick and reliable answers and give enough information about most of the topics. Furthermore, ChatGPT allows users to have more in-depth conversations. The concern about the impact role of ChatGPT on educational and social institutions has led to some reactions from academic institutions, and school districts were hasty and took action to block artificial intelligence platforms from schools, which they quickly retracted in the aftermath. On the other hand, some scholarly academic journals, including our Arab World English Journal, have announced guidelines for article authors. Authors must disclose the use of generative AI and AI-assisted technologies in the writing process. While we are editing the final draft of this volume (April 2024), OpenAI CEO Sam Altman announced in a statement that OpenAI has opened its first office in Asia, Japan, and is providing a GPT-4 custom for the Japanese language; it is three times faster its predecessor. OpenAI aims to expand ChatGPT’s global presence. After releasing ChatGPT, which is backed by Microsoft, OpenAI has emerged as a leading company in the history of computing. In this special issue, we addressed the impact of ChatGPT and AI-assisted technologies, an essential topic that will constitute the main features of education and publications in the twenty-first century. The issue contains 21 articles from eight countries (China, Kuwait, Iraq, Oman, Malaysia, Morocco, Saudi Arabia, and Ukraine). It covers a broad spectrum of related topics: Academic writing, the pedagogical potential of infinigoChatIC, Grammar Teaching and Learning in EFL Classes, English Language Proficiency in EFL Classrooms, Grammar Teaching and Learning in EFL Classes, EFL Essay Writing, Distance Learning Environment, Evaluation of the Translation, Improve the Research Writing Process, Role of Artificial Intelligence in Education, Language Learners’ Critical Thinking, Naruto Fan Fiction Writing, and the Impact of ChatGPT on Future Academics. We seize this opportunity to extend our heartfelt thanks to the authors who have shared their research with our Arab World English journal. We anticipate that the articles in this volume will attract a diverse readership and deepen our understanding of ChatGPT’s role in learning and teaching and its associated challenges. We also hope these papers will provide a thought-provoking perspective for faculty members and students to consider ChatGPT’s impact on writing and related assignments.

The role of artificial intelligence and fintech in promoting eco-friendly investments and non-greenwashing practices in the US market

This study explores the intricate connections among financial technology (FinTech), artificial intelligence (AI), and eco-friendly markets in the US, shedding light on their dynamic interplay and implications for sustainable investment and policy strategies. Specifically, our research delves into the transformative roles of FinTech and AI in broadening financial access, fostering green financing initiatives, and aligning financial practices with environmentally conscious objectives. We also investigate market reactions among the AI, FinTech, non-greenwashing, and eco-friendly markets during exogenous shocks, offering valuable insights into these markets' interconnectedness. An innovative connectedness approach, the R2 decomposed measures, is employed to capture the contemporaneous and lagged spillover effects using daily data from December 19, 2017, to November 1, 2023. We also focus on constructing a minimum connectedness portfolio using the time-varying parameter vector autoregressive approach. The findings reveal significant volatility connectivity within these intergroups, emphasizing the need for sustainable tech finance policies and real-time monitoring systems to address market fluctuations. Overall, this study contributes to an underexplored area by providing empirical evidence and valuable implications for scholars and policymakers, and can help in guiding sustainable investment and policy strategies aligned with zero-emissions agendas.

NAVIGATING THE FUTURE: INTEGRATING AI AND MACHINE LEARNING IN HR PRACTICES FOR A DIGITAL WORKFORCE

As organizations navigate the complexities of the digital age, the role of Human Resources (HR) is evolving to meet the demands of a digital workforce. This review explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) in HR practices to enhance efficiency, effectiveness, and employee satisfaction in the digital era. AI and ML technologies offer HR departments the opportunity to streamline operations, improve decision-making processes, and enhance employee experiences. By leveraging AI and ML, HR professionals can automate routine tasks such as recruitment, onboarding, training, and performance evaluation, allowing them to focus on more strategic initiatives that drive organizational success. One of the key advantages of integrating AI and ML in HR practices is the ability to personalize employee experiences. These technologies can analyze large volumes of data to identify patterns and trends, enabling HR professionals to tailor programs and policies to meet the unique needs of individual employees. This personalization can lead to higher levels of employee engagement, satisfaction, and retention. Furthermore, AI and ML can help HR departments make more informed decisions by providing data-driven insights. These technologies can analyze employee data to identify areas for improvement, predict future trends, and develop strategies to address challenges proactively. By leveraging these insights, HR professionals can make strategic decisions that align with the organization's goals and objectives. However, integrating AI and ML in HR practices also presents challenges, such as data privacy concerns, ethical considerations, and the need for upskilling HR professionals to use these technologies effectively. Organizations must address these challenges to realize the full potential of AI and ML in HR practices. In conclusion, integrating AI and ML in HR practices offers organizations the opportunity to enhance efficiency, effectiveness, and employee satisfaction in the digital age. By leveraging these technologies, HR departments can streamline operations, personalize employee experiences, and make more informed decisions that drive organizational success. As organizations increasingly turn to digital solutions, the role of artificial intelligence (AI) and machine learning (ML) in Human Resources becomes pivotal. This paper will focus on how AI and ML are being integrated into HR functions such as recruitment, onboarding, and employee engagement. It will also discuss the ethical implications and the challenges of maintaining human touch in an increasingly automated workplace. Case studies of companies leading in digital HR practices will be highlighted to provide real-world insights. Keywords: Digital Force, HR Practices, AI, Machine Learning, Future.

Role of Artificial Intelligence in VLSI Design: A Review

Abstract:: Artificial intelligence (AI) related technologies are being employed more and more in a range of industries to increase automation and improve productivity. The increasing volumes of data and advancements in high-performance computing have led to a sharp increase in the application of these methods in recent years. AI technology has been widely applied in the field of hardware design, notably in the design of digital and analogue integrated circuits (ICs), to address challenges such as rising networked devices, aggressive time-to-market, and everincreasing design complexity. However, very little attention has been paid to the issues and problems related to the design of integrated circuits. The authors of this article review the stateof- the-art in AI for circuit design and optimization. AI offers knowledge-based technologies that give challenges a foundation and structure. A technology known as AI makes it possible for machines to mimic human behavior. Data in all formats, including unstructured, semistructured, and structured, can be processed by AI. It is crucial to incorporate all of the features and levels of the many CAD programmes into a single, cohesive environment for creation, as was mentioned in the section that came before this one. Consequently, the application of AI automation helps to enhance the effectiveness and efficiency of CAD's performance.

Artificial Intelligence for Water Consumption Assessment: State of the Art Review

AbstractIn recent decades, demand for freshwater resources has increased the risk of severe water stress. With the growing prevalence of artificial intelligence (AI), many researchers have turned to it as an alternative to linear methods to assess water consumption (WC). Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study utilized 229 screened publications identified through database searches and snowball sampling. This study introduces novel aspects of AI's role in water consumption assessment by focusing on innovation, application sectors, sustainability, and machine learning applications. It also categorizes existing models, such as standalone and hybrid, based on input, output variables, and time horizons. Additionally, it classifies learnable parameters and performance indexes while discussing AI models' advantages, disadvantages, and challenges. The study translates this information into a guide for selecting AI models for WC assessment. As no one-size-fits-all AI model exists, this study suggests utilizing hybrid AI models as alternatives. These models offer flexibility regarding efficiency, accuracy, interpretability, adaptability, and data requirements. They can address the limitations of individual models, leverage the strengths of different approaches, and provide a better understanding of the relationships between variables. Several knowledge gaps were identified, resulting in suggestions for future research.

Role of Artificial Intelligence in Workforce Management: An Overview of its Benefits

Organisations have long relied on arduous manual processes in managing their workforce planning activities. HR professionals are under intense pressure to keep up pace with the dynamic workforce scheduling in light of the persistent labour market disruptions. Recent advancements in Artificial Intelligence (AI) have made it possible for advanced data applications that help optimize many business processes. Managements are now enabled with AI solutions that range from automation of routine tasks to assisting them create strategies for talent management and retention. This research paper analysis how AI-powered tools are transforming the HR landscape and are being increasingly utilised by business entities to optimize their workforce management functions. The indispensable role played by AI in workforce planning in the retail, healthcare and finance industries in overcoming their challenges and optimizing their performance is examined. This research study demonstrates how adoption of AI has led to increased productivity and efficiency and emphasizes the crucial role it would continue to play in the future HR workforce management functions.

Navigating the Artificial Intelligence Frontier: Perceptions of Instructors, Students, and Administrative Staff on the Role of Artificial Intelligence in Education in the Sultanate of Oman

This study examines the perceptions of instructors, students, and administrative staff on the role of ChatGPT in Oman’s educational setting. This study is significant as it provides insights into the extent to which Artificial Intelligence is used in education and provides guidance for future plans. Examining the perceptions of the various stakeholders in an education setting in Oman provides valuable information for higher learning institutions that are keen on embracing new technology while keeping to the traditional education values. The study utilized focus group discussions to gather the data from the instructors, students, and administrative staff. The findings revealed that ChatGPT’s instrumental role is in refining content, especially among students, administrative staff, and instructors who are non-native English speakers. Administrative staff and instructors highlighted its efficacy in drafting emails, indicating Artificial Intelligence’s potential to improve routine cognitive tasks. Students appreciated ChatGPT for explaining complex academic tasks. However, concerns surface from the instructors regarding over-reliance on Artificial Intelligence and potential loss of academic integrity, resonating with previous literature. These findings are contextualized within Oman’s unique socio-cultural and educational settings. Given the emergent nature of Artificial Intelligence in Oman’s education, the study offers insights that provide a foundation for future research and guide policymaking.

Empowering Indian Agriculture: The Role of Artificial Intelligence

Empowering Indian Agriculture: The Role of Artificial Intelligence