This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the studies—, enhance predictive accuracy and adaptability to complex, dynamic project environments. Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. ML models, including ANNs and deep learning models, represent approximately 70% of the reviewed studies, demonstrating a clear trend toward the adoption of advanced AI techniques. On average, deep learning models perform with 85–90% accuracy in cost estimation, making them highly effective for handling complex, nonlinear relationships and large datasets. Machine learning models achieve an average accuracy of 75–80%, providing strong performance, particularly in industries like road construction and healthcare. Regression models typically deliver 70–80% accuracy, being more suitable for simpler cost estimations where the relationships between variables are linear. Hybrid models combine the strengths of different algorithms, achieving 80–90% accuracy on average, and are particularly effective in complex, multi-faceted projects. Overall, deep learning and hybrid models offer the highest accuracy in cost estimation, while machine learning and regression models still provide reliable results for specific applications.

The core theme of the current investigation is to explore the application of an IoT framework protocol based on an Arduino platform designed to optimize sunflower seed production in Uzbekistan based on the levels of air quality and soil moisture. In essence, the need is to give best actionable intelligence to farmers and the stakeholders in the agricultural sector on crop growing opportunities. The above proposed system involves the use of air quality sensors MQ-135 for instance, and soil moisture sensors. The sensors are connected to Arduino boards to collect necessary data and measurements are recorded every 30 minutes using available WiFi and Bluetooth modules for continuous monitoring. The simulation reveals air quality data of the sunflower fields of the present scenario to be an average at PM2.5 is of 75 µg/m³, which poses danger to the wellbeing of plants. It is further expected that the use of MQ-135 air quality sensors will decrease the overall average of PM2.5 to 45 µg/m³, the local authorities managed to cut emissions by 40% as part of the EU plan. At the present time, the content of the field moistures is 15 % VWC, which is not favorable for sunflower development. Soil moisture sensors for accurate irrigation control is another advance that requires soil moisture levels to rise to 25% vadium weight (VWC), up from 66. 7% improvement. Therefore, it means that the yields from the sunflower seeds are expected to rise from the current average of 1, 500Kg/ha to 1, 875 Kg/ha, which is a 25% enhancement. These results imply that IoT systems developed on the Arduino platform may be used to oversee environmental alteration and increase the agricultural crop yield by a wide margin. The possibility was identified to achieve significant outcomes in increasing sunflower seed production based on this framework when implemented on a larger scale for the benefit of Uzbek farmers.

Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S–O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).

Virtual influencers have become a key tool in modern marketing due to their high controllability and cost-effectiveness, gradually replacing traditional influencers and celebrities. This study focuses on virtual influencers as brand endorsers, based on signaling theory and attachment theory, exploring the influence of five key characteristics - controllability, interactivity, parasocial relationship, influencer-product congruence, and source credibility on the purchase intension of Chinese Generation Z consumers. The study used a quantitative approach to collect data through a structured questionnaire distributed to 403 Chinese Generation Z consumers, with the sample covering different city tiers to reflect regional diversity. The data were subjected to exploratory factor analysis (EFA), and the structural equation modeling using the partial least squares (PLS-SEM) method. The results show that all five key characteristics of virtual influencers significantly and positively influence consumer purchase intentions. Brand equity moderated the effects of controllability and influencer- product congruence on purchase intention, while emotional attachment amplified the roles of interactivity, parasocial relationships, and source credibility. However, some of the moderating effects did not reach statistical significance, suggesting that the traditional moderating structure may not adequately reflect the dynamic impact of the virtual influencer's non-human characteristics and its high controllability. This study expands the application of signaling theory and attachment theory to reveal the unique role of brand equity and emotional attachment in the context of virtual influencer. The findings provide guidance for companies to optimize their virtual influencer marketing strategies.

A critical part of the national economy is small and medium enterprises (SMEs). SME owners are vital contributors to the overall economy. Due to their limited capital and assets, they are more vulnerable. In comparison to their contribution, the value of assessing SME owner's depression, anxiety, and mental stress has been very minimal during the COVID‐19 outbreak. Firms were forced to close due to lockdown, and they faced substantial business losses. Thus, this study aims to investigate SME owners' psychological distress due to business losses during this pandemic. The study used psychological parameters: Depression, Anxiety, Stress Scale‐21 (DASS‐21) to examine SME owners' psychological distress. A total number of 217 owners were surveyed through a judgmental sampling technique using a structured questionnaire. Data were analyzed employing partial least square‐based structural equation modeling (PLS‐SEM). The findings showed that DASS‐21 parameters and fear of business loss affected psychological distress. Besides, fear of business loss increases psychological distress, whereas government support lessens the distress. Theoretically, this study extended the scope of DASS‐21 scale and contributed to the literature of psychology. In terms of policy implications, this study provides useful information for government, policymakers, and SME owners about the effects mentioned above.

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