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.

Nowadays, communication can be conducted at high speed and conveniently through computer networks. In addition, devices in network systems can store data for extended periods. However, communication or storage over the network may lead to security issues or loss of data integrity. Therefore, data security mechanisms should be used to verify and maintain confidentiality and integrity. This research aims to evaluate which algorithm can verify data integrity and identify message authors in the least amount of time. The algorithms studied are Rivest–Shamir–Adleman (RSA), Hash-based Message Authentication Code–Secure Hash Algorithm 256 (HMAC-SHA256), HMAC-SHA384, and HMAC-SHA512, all of which verify data integrity and authenticate message authors. We compared the performance of all algorithms using ten different data sizes ranging from 100 to 1000 MB, with twenty datasets for each size, and evaluated their throughput and bandwidth. The results show that the HMAC-SHA256 algorithm requires the least execution time and therefore demonstrates the highest efficiency. In addition, the algorithm becomes more efficient as the data size increases, followed by HMAC-SHA512, HMAC-SHA384, and RSA, respectively. In future work, studying other algorithms that are capable of verifying data integrity and sender authenticity, such as digital signatures using RSA, Elliptic Curve Cryptography (ECC), and Galois Message Authentication Code (GMAC), or developing applications using HMAC-SHA256 for data integrity and sender authentication, will provide further insights into selecting the most appropriate algorithm for specific applications.

A Development of Supporting System for Historical Heritage Based Tourism

Rice cultivation in climate-sensitive regions necessitates adaptive irrigation and nutrient management strategies to enhance resource utilization efficiency and mitigate operational uncertainty. This study investigated the operational feasibility of an Internet of Things (IoT)-based monitoring and recommendation system for real-time soil moisture and nutrient-related operational monitoring in large-scale rice farming environments in Thailand. An integrated IoT-assisted monitoring and recommendation framework comprising sensing, communication, analytics, and recommendation components was developed and evaluated under practical field-deployment conditions. The system incorporated soil moisture monitoring and nutrient-related operational sensing, cloud-based data processing, machine learning-assisted prediction, and mobile notification services to support irrigation and fertilizer management. A comparative evaluation between conventional and IoT-assisted management conditions revealed lower irrigation water use (947.38 vs. 7638.38 m³/ha), reduced fertilizer utilization (41.40 vs. 347.56 kg/ha), and lower production costs (4230.88 vs. 30,664.69 THB/ha) under IoT-assisted conditions. Average profit also increased from 2357.68 to 23,920.00 THB/ha. User evaluation indicated high overall satisfaction (mean = 4.28/5.00). The findings suggest that integrating IoT-based sensing, machine learning-assisted prediction, and optimization-driven recommendation workflows within a unified field-deployment framework may improve adaptive irrigation management, resource-allocation efficiency, and operational decision support under climate-sensitive rice cultivation environments.

Artificial intelligence (AI) can manage tourism by optimizing, personalizing the experience, and enhancing user interactions. This research presents the Ayutthaya tourism platform independent model (ATPiM), an intelligent tourism application that integrates a domain-specific language (DSL) designed for chatbot development with machine learning algorithms that generate personalized recommendations based on user preferences, historical data, and real-time contextual influences. This pre-experimental design measures performance on parameters such as response time, recommendation accuracy, and system latency. The outcomes indicate that the mean time taken to respond to a user's query was 2.3 seconds, with 88.5% recommendation accuracy, and no latency. The AI-based recommendation system achieved 89.7% accuracy at destinations, 87.2% at accommodations, 90.3% at itineraries, and 85.6% at activities, with corresponding recalls of 85.4%, 83.5%, 88.1%, and 80.2% respectively. Although these results are promising, a 6.2% error rate for the advanced search, along with data security are some of the remaining issues. The findings reveal that the development of new user-centric and sustainable solutions for tourism, which leverage state-of-the-art natural language processing approaches, can enhance data security and provide additional new technologies, such as augmented reality (AR) and blockchain, for use in tourism.

Rice farming in Central Thailand relies heavily on controlled irrigation, making efficient water management critical for sustainable production. This study examines how production input costs influence irrigation water demand, measured as irrigation water volume per unit area (m3∙ha−1), using panel data from 215 rice farmers in Sena District, Phra Nakhon Si Ayutthaya Province, covering the period 2016–2023. The dataset comprises 1,720 farm-year observations across approximately 7.58 km2 of irrigated area. A log-linear Cobb–Douglas model estimated using panel regression techniques is applied to quantify the elasticities of irrigation water demand with respect to production input costs. Because all variables are expressed in logarithmic form, the estimated coefficients are directly interpretable as elasticities. The results indicate that fertiliser (β = 0.210), pesticide (β = 0.165), machinery (β = 0.302), and fuel costs (β = 0.128) have positive and statistically significant effects on irrigation water demand per rai, whereas seed and labour costs are not statistically significant. For example, a 1% increase in fertiliser expenditure is associated with a 0.21% increase in irrigation water demand per rai, holding other factors constant. The model explains a substantial share of the variation in irrigation water demand (R2 = 0.63), indicating strong explanatory power. The findings highlight that chemical input use and mechanisation are key drivers of irrigation intensity in rice farming. These results underscore the importance of integrated water–input management strategies, including improved nutrient scheduling, energy- efficient pumping technologies, and data-driven irrigation planning, to enhance water-use efficiency and support sustainable rice production in Thailand.