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Multimedia data is crucial in the military, medical, forensics, social, etc., to transmit a large amount of data. Security of this sensitive information is the primary issue. This paper uses Latin square and machine learning techniques such as neural networks and genetic algorithm to design an image encryption algorithm. A new neural network-based pseudorandom number generator is proposed to generate a chaotic sequence for various applications. Encryption key images are designed using Latin squares in the finite field. Further, the Latin squares are XOR with the input matrix to get the encrypted images. The proposed algorithm is iterated a finite number of times to generate a cipher image population. Randomly two parents are chosen from the generated population, and row and column arrangements produce offspring. A genetic algorithm is the optimization technique used for the best encrypted image search. The pixel correlation value serves as a fitness function. Finally, the least correlated cipher image is obtained from the genetic algorithm applied to the parent and offspring of the population generated from the encryption algorithm. The simulation results from the proposed image encryption model surpass many communication channel attacks and perform better when compared to existing image security algorithms. [ABSTRACT FROM AUTHOR]

Time-split cross-validation is broadly recognized as the gold standard for validating predictive models intended for use in medicinal chemistry projects. Unfortunately this type of data is not broadly available outside of large pharmaceutical research organizations. Here we introduce the SIMPD (simulated medicinal chemistry project data) algorithm to split public data sets into training and test sets that mimic the differences observed in real-world medicinal chemistry project data sets. SIMPD uses a multi-objective genetic algorithm with objectives derived from an extensive analysis of the differences between early and late compounds in more than 130 lead-optimization projects run within the Novartis Institutes for BioMedical Research. Applying SIMPD to the real-world data sets produced training/test splits which more accurately reflect the differences in properties and machine-learning performance observed for temporal splits than other standard approaches like random or neighbor splits. We applied the SIMPD algorithm to bioactivity data extracted from ChEMBL and created 99 public data sets which can be used for validating machine-learning models intended for use in the setting of a medicinal chemistry project. The SIMPD code and simulated data sets are available under open-source/open-data licenses at github.com/rinikerlab/molecular_time_series. [ABSTRACT FROM AUTHOR]

Optimization problems aim to minimize or maximize an objective function while fulfilling related constraints. This objective function may be a single or multi-objective optimization. Many studies have been conducted on using these optimization problems in civil and construction engineering, especially for the various machine learning techniques and algorithms that have been developed for fiber reinforced polymer (FRP) applications in the rehabilitation and design of RC structures. FRP is considered the most effective and superior technique for strengthening and retrofitting due to its significant benefits over traditional methods, which have numerous drawbacks, as well as the importance of structural strengthening as a cost-effective and practical option. In this research, an insight into how to apply algorithms and machine learning approaches to optimize FRP applications in civil and construction engineering is presented, as well as a detailed analysis of the various optimization strategies used and their findings. A total of 18 case studies from previous research were discussed and critically evaluated, and they were categorized into six groups according to the algorithm or machine learning technique utilized. Based on the case studies investigated in this study, the genetic algorithm was found to be the optimal algorithm utilized for optimizing FRP applications. The result of this research provides a useful guideline for future researchers and specialists. [ABSTRACT FROM AUTHOR]