The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed.

Cryptography is one of the common approaches to secure private data and cryptanalysis involves breaking down a coded cipher text without having the key. Cryptanalysis by brute force cannot be accepted as an effective approach and hence,...

Substitution boxes (S-boxes) are key components of symmetrical cryptosystems, acting as nonlinear substitution functions that hide the relationship between the encrypted text and input key. This confusion mechanism is vital f... | Find, read and cite all the research you need on Tech Science Press

Learning Management Systems has become one of the teaching tool same as the pen, pencil, blackboard, book, and notebook. Learning Management System (LMS) is defined and types that are derived from this system such as those focused only on the curriculum and those focuses on the management of the education system, and the combination of both are explained. The properties and features that must exist are identified and showed so that it can be called a learning management system. The name and the experiences of countries in terms of the use of this system as well as the benefits resulting from the user experiences have motivated schools and educational institutions to start the establishment of such systems. The main purpose of this study is, an online plagiarism detection system will be developed and integrated into Moodle so that it can be used by teachers to detect cheating and plagiarism cases in students submitted answers.

With the internet and modern mobile technologies, health-related information is readily available, and thus, the security aspect of health information is at great risk. Confidentiality and protection of medical information regarding patients are of prime concern in the context of sharing such data with different healthcare providers. On one hand, Electronic Health Record Systems (EHRS) and online sites have proved to be hassle-free ways of exchanging medical information between health professionals. On the other hand, data security issues remain a concern. The proposed paper presents an improvement in the security mechanism of EHRS by utilizing the Lion Optimization Algorithm (LOA) and the Secure Hash Algorithm (SHA-256) algorithm, which is a better security mechanism, and elliptic curve cryptography. This design will use LOA for the generation of an Elliptic Curve Cryptography (ECC) private key to improve the security of data storage in EHRs. The design will be further justified by comparing the encoding and decoding time against the other techniques such as ECC-GA-SHA-256. The results depict that the proposed model indeed gives faster encoding and decoding times. It also outperforms other encryption methods, as it presents encoding and decoding times higher by more than 18.89% when compared to those methods. This means there is great potential for the proposed approach to boost both the security and performance of EHRS. That is, this work could be one of the effective solutions to the security-related challenges of the EHR systems, as it was designed using LOA with SHA-256 and ECC.

Today it is necessary to keep information secure and cryptography is the most common technique for data security. The Vigenere cipher, one of the polyalphabetic encryption algorithms, has been used in the history by substitution of the plaintext letters with other alphabet letters using a secret keyword and a systematic table. In order to make the ciphertext readable with a keyless procedure, the cryptanalysis technique is used. However, extracting all possible permutations of the letters is exhaustive or frequency analysis is ineffective to extract the letters from the cipher. Therefore, this study aims to propose an efficient polyalphabetic Vigenere cipher cryptanalysis using Differential Evolution algorithm on English and Turkish texts at different lengths. The efficiency of the Differential Evolution algorithm is compared to those of Genetic Algorithm and Particle Swarm Optimization algorithms in terms of the number of key letters recovered correctly. The results show that Vigenere cipher analysis using Differential Evolution algorithm is more effective in polyalphabetic cryptanalysis.