System for Water Quality Monitoring and Distribution

Water plays a vital role in the creation of human being and other natural phenomena. More than 80% of the resources is surrounded by water but in that only 20% is good for consumption others are fully polluted and contaminated. Now a days water is more polluted, and even supplied in a very lesser level so to check and monitor the quality of the water we mainly using a number of sensors are used to monitor the water’s quality and distribute it to the less fortunate. The quality of the water is affected by several parameters. Water is provided from difference resources like lake, pond, well, ground water, oceans etc.so these waters are not good for consumption Therefore, our goal is to assess the water’s quality while keeping an eye on the flow and level of the water. It is intended to use a variety of cutting-edge devices to check various water quality system parameters

Privacy-Preserving Data Mining and Analytics in Big Data

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets

System for Water Quality Monitoring and Distribution

Water plays a vital role in the creation of human being and other natural phenomena. More than 80% of the resources is surrounded by water but in that only 20% is good for consumption others are fully polluted and contaminated. Now a days water is more polluted, and even supplied in a very lesser level so to check and monitor the quality of the water we mainly using a number of sensors are used to monitor the water’s quality and distribute it to the less fortunate. The quality of the water is affected by several parameters. Water is provided from difference resources like lake, pond, well, ground water, oceans etc.so these waters are not good for consumption Therefore, our goal is to assess the water’s quality while keeping an eye on the flow and level of the water. It is intended to use a variety of cutting-edge devices to check various water quality system parameters

Privacy-Preserving Data Mining and Analytics in Big Data

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data mining methods, sharing or pooling data might have serious privacy implications. On the other hand, privacy-preserving data mining strategies concentrate on creating procedures and algorithms that enable analysis without jeopardizing personal information. Finally, privacy-preserving data mining and analytics in the Big Data age bring important difficulties and opportunities. An overview of the main ideas, methods, and developments in privacy-preserving data mining and analytics are given in this abstract. It underscores the value of privacy in the era of data-driven decision-making and the requirement for effective privacy-preserving solutions to safeguard sensitive personal data while facilitating insightful analysis of huge datasets