Estimating water quality has existed as one of the vital factors embarked on the planet in the present eons. This paper illustrates a water quality estimate based on the Linear Discriminant Analysis (LDA) technique. Weighted arithmetic index technique is used in the computation of the Water Quality Index (WQI). At that moment, the LDA is linked to the dataset, and the ultimate principal WQI dynamics have been determined. Subsequently after predicting the WQI, Light Gradient Boosted Machine (LGBM) classification is performed in the LDA. Lastly, the LGBM classifier is activated to label the water quality. This proposed LGBM with LDA technique is demonstrated and evaluated on a Gulshan Lake-related dataset. The results show 96% forecast accuracy for the LDA and 100% categorization accuracy for the Light Gradient Boosted Machine classifier system that indicate consistent interpretation linked over the futuristic prototypes. This innovative model LDA-LGBM is aimed at enhancing the prediction of water quality and its classification through AI - ML approach.

Agriculture is a noteworthy and vibrant domain in the fiscal evolution of the globe. With populationin progress, climatic situation and assets, and agriculture turn out dazed to be a crucial task to realize the necessities of the future population. Intelligent precision agriculture/intelligent smart farming has transpired as an innovative tool to tackle hovers of the future ahead in automated agricultural sustainability by leading Artificial Intelligence (AI) in agriculture automation.AI unravels critical farm labor challenges by improving or reducing work and lessening the necessity of numerous workers. Agricultural AI aids in reaping harvests quicker than human employees at a greater quantity, further precise in categorizing and eradicating unwanted plants, also dropping cost and menace. This process motivates the cutting-edge technologies capitulating the machine capability to learn by sourcing Bootstrapped Meta-learning also reinforcing with rewards as maximum crop yields and minimum resource utilizations as well as within time limits. AI empowered farm machinery is the key constituent of the future agriculture revolution ahead. In this exploratory work, an efficient automation of AI application in the field of agriculture sustenance is ensured for receipt of the most obtainable aids as outcomes and inhibiting the applied assets. Fixing the precise real-time issues trailed by unravelling it for agricultural augmentation or amplification thereby leads to the global best future agriculture.

Athletics bureaucrats round the globe are tackling implausible encounters owing to the partial methods of customs executed by the athletes to progress their enactment in their sports. It embraces the intake of hormonal centred remedies or transfusion of blood to upsurge their power and the effect of their coaching. On the other hand, the up-to-date direct test of discovery of these circumstances embraces the laboratory-centred technique viz restricted for the reason that of the cost factors, handiness of medical experts, etc. This ends us to pursue for indirect assessments. By the emergent curiosity of Artificial Intelligence (AI) in healthcare, it is vital to put forward a process built on blood factors to advance decision making. In this research script, a statistical and machine learning (ML) centred tactic was suggested to ascertain the concern of doping constituent rhEPO in blood units.

Agriculture is a significant and vivacious domain in the fiscal evolution of the globe. With current population, climatic conditions and resources, agriculture turns out to be a challenging task to fulfill the requirements of the future population. Intelligent Precision agriculture also known as intelligent smart farming has emerged as an innovative tool to address current challenges in automated agricultural sustainability. This mechanism that drives this cutting edge technology, that is the machine learning (ML) giving the machine ability to learn without being explicitly programmed reinforced with rewards. AI and ML together with IoT (Internet of Things) enabled farm machineries are key components of the future agriculture revolution ahead. In this work, a systematic Gaussian Quadrature numerical analysis of ML applications in the field of agriculture is done. Fixing the right real-time problems followed by solving it for agricultural augmentation or amplification thereby leading to global best.

A novel conception of Knowledge Lake, i.e., a Contextualized Data Lake is to be soundly educated. The deliberated big-data practices pave a means for the erection of Intelligent Knowledge Lakes and that being the resources for big-data applications and analytics. This analysis likewise opens the welfares, disputes, and exploration prospects of Intelligent Knowledge Lakes. Data Science is launched as an influential discernment through businesses. Organizations today are dedicated on transforming their facts into ultra-practical intuitions. This work is challenging, as in present day’s intelligence, amenity and cloud customary budget trades accumulate immense aggregates of unprocessed data after a variety of funds. Data Lakes are familiar as a packing depository that fetch concurrently the unprocessed data in its innate set-up (sustaining to NoSQL from relational databases) which is crucial. The logic behind Data Lake is to stockpile unprocessed data and let the data analyst resolve the way to curate them well ahead of reviewing the idea of Knowledge Lake, which is an anecdotal Data Lake. The Intelligent Knowledge Lake stipulate the basis for big data analytics by robotically curating the unprocessed data in the Data Lake grooming these for stemming intuitions via programmed interactive real-time optimized data wrangling in intelligent knowledge lakes. Computerization of an exposed free public Data and Knowledge Lake amenity provides developers and researchers a distinct REST API to systematize, curate, catalog and interrogate their data and metadata in the Lake for a longer time. It administers manifold database/databank know-hows (from Relational to NoSQL) that deals with an inherent scheme for data security, curation, and provenance.