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This study explores the interconnections between new farming practices, smart agricultural technology, mathematical skills, data-driven decision-making, and community learning in areas commonly affected by drought. Using a statistical method known as Structural Equation Modeling (SEM) and data from 320 farmers, the study explores how new farming ideas encourage smart practices that improve math skills. It also demonstrates how smart farming creates an environment where data helps inform decision-making, which benefits community learning. The results indicate that New Theory Agriculture (NT) encourages Smart Agriculture (SA) engagement, thereby facilitating both Mathematical Literacy (ML) and Data-Driven Decision-Making (DD). Engagement in SA is closely linked to improvements in ML, which, in turn, strengthen DD abilities. ML plays a central role by serving as a bridge between SA and DD, which, in turn, directly affects Community Learning Outcomes (CL). The findings show that NT fosters community-level outcomes by first building SA and ML, both of which shape DD and ultimately enhance CL, clarifying the sequence of concept connections. The findings reveal that implementing NT and smart technology in agriculture systematically enhances farmers’ resource management and the evolution of mathematical and data skills beyond formal education. The research demonstrates how cognitive skills, technological participation, and collective learning are linked within the community: NT leads to SA engagement, which develops ML, enables DD, and produces CL. The study discusses implications for community education, digital agriculture policy, and rural capacity development, suggesting that future longitudinal or experimental studies could clarify how these connections change over time.

Ciphertext-policy attribute-based encryption (CP-ABE) is a suitable solution for the protection of data privacy and security in cloud storage services. In a CP-ABE scheme which provides an access structure with a set of attributes, users can decrypt messages only if they receive a key with the desired attributes. As the number of attributes increases, the security measures are strengthened proportionately, and they can be applied to longer messages as well. The decryption of these ciphertexts also requires a large decryption key which may increase the decryption time. In this paper, we proposed a new method for improving the access time to the CP using a new elliptic curve that enables a short key size to be distributed to the users that allows them to use the defined attributes for encryption and decryption. Each user has a specially created key which uses the defined attributes for encryption and decryption based on the Diffie-Hellman method. After the implement, the results show that this system saves nearly half of the execution time for encryption and decryption compared to previous methods. This proposed system provides guaranteed security by means of the elliptic curve discrete logarithmic problem.

The main purpose of passwords is to prevent unauthorized people from accessing the system. The rise in internet users has led to an increase in password hacking, which has resulted in a variety of problems. These issues include opponents stealing a company's or nation's private information and harming the economy or the organization's security. Password hacking is a common tool used by hackers for illegal purposes. Password security against hackers is essential. There are several ways to hack passwords, including traffic interception, social engineering, credential stuffing, and password spraying. In an attempt to prevent hacking, hashing algorithms are therefore mostly employed to hash passwords, making password cracking more difficult. In the suggested work, several hashing techniques, including message digest (MD5), secure hash algorithms (SHA1, SHA2, and SHA3) have been used. They have become vulnerable as a result of being used to store passwords. A rainbow table attack is conceivable. Passwords produced with different hash algorithms can have their hash values attacked with the help of the Hashcat program. It is proven that the SHA3 algorithm can help with more secure password storage when compared to other algorithms.

This research proposes a solution to improve the system for monitoring relevant environmental parameters using sensors for flood mitigation. Sensors are used to collect data regarding farm flood situation. The collected data are trained for a classification model to activate the solar-powered water pump to mitigate flood incidents in a flood-prone area. The system helps farmers to monitor real-time environmental parameters relevant to farming operations and flood including soil moisture level, water level, and water flow speed in a nearby canal that provides water to the farm. To reduce flood damage, the system assists to drain the excessive water to prevent prolonged submerging of the crop. The devices are designed to use the electricity from solar power, so the system is practically used outdoor where an electricity cord is difficult to setup. Experimental results show that the sensing data from the deployed sensors are accurate. The generated prediction models give the high performance with average of 1.0, 0.97, and 0.93 F-1 score for no-flooding, mild-flooding, and severe flooding respectively.