Geopolymer concrete could be the best alternative to ordinary Portland cement concrete due to its higher performance in any severe condition. It reduces the carbon footprints to a very higher level. Machine learning methods are the future of the construction industry because it predicts the mechanical strengths of concrete mix design on the basis of their constituents without destructive test conduction. This study is aimed at developing the models to predict the mechanical strengths and validate them with the actual results. After the experimental investigation, we found the results of the mechanical (including compressive, splitting tensile, and flexural tensile) strength. The M2 mix of geopolymer concrete got the highest mechanical strengths whereas the M5 mix gets the lowest mechanical strengths among all the mix designs. The machine learning methods ANN (artificial neural network) and random forest are used to develop the models based on mixed experimental results. Mechanical strength results are taken as outputs, and mixed constituents are taken as inputs for training and testing. The performance of predicted results is checked based on R², MAE (mean absolute error), RMSE (relative mean square error), RAE (relative absolute error), and RRSE (root‐relative square error). Random forest models show the best prediction to the ANN models because it shows the negligible error between actual and predicted values. The R² value is 1 of 12 predicted results out of 15 by the use of random forest methods. So it is most suitable to predict the strength of geopolymer concrete based on their constituent’s material quantity.

Geopolymer concrete is sustainable, economical, eco-friendly, durable, and high-strength concrete. Geopolymer is a name for the bonding that occurs during the binding of materials in alkaline conditions. Due to the presence of high silica and alumina content, pozzolanic materials could be used as binding materials in the GPC. This research aims to check the sustainability and cost analysis of both GPC and conventional concrete with their physical, chemical, and mechanical properties. The experimental investigation analyzes both GPC and OPC concretes physical, chemical, and mechanical properties for the M30 mix design and analyzes the concretes cost and sustainability. The experimental investigation shows that the setting time, density, and drying shrinkage of conventional concrete are higher than the GPC. The compressive strength of the GPC and OPC concretes both showed similar trends at the 28-day strength, but the initial three-day strength of the GPC concrete was much higher than the OPC concrete. The splitting tensile strength and flexural strength of the GPC specimens are slightly higher than the OPC concrete mix specimens. The OPC concretes elastic modulus is slightly higher than the GPC mix design, whereas the Poissons ratio of the OPC concrete is slightly lower than the GPC specimens. The GPC specimens have higher thermal stability up to 800°C. The GPC utilizes industrial solid waste like fly ash and slag as a binding material and is activated by an alkaline solution containing NaOH and Na₂SiO₃ in the design mix. Therefore, the GPC has less embodied energy compared to the OPC concrete. The cost of the GPC at a bulk level reduced the cost of up to 40 of the OPC concrete.

In today’s digital world, the Internet is an essential component of communication networks. It provides a platform for quickly exchanging information among communicating parties. There is a risk of unauthorized persons gaining access to our sensitive information while it is being transmitted. Cryptography is one of the most effective and efficient strategies for protecting our data and it are utilized all around the world. The efficiency of a cryptography algorithm is determined by a number of parameters, one of which is the length of the key. For cryptography, key (public/private) is an essential part. To provide robust security, RSA takes larger key size. If we use larger key size, the processing performance will be slowed. As a result, processing speed will decrease and memory consumption will increase. Due to this, cryptographic algorithms with smaller key size and higher security are becoming more popular. Out of the cryptographic algorithms, Elliptic Curve Cryptography (ECC) provides equivalent level of safety which RSA provides, but it takes smaller key size. On the basis of key size, our work focused on, studied, and compared the efficacy in terms of security among the well-known public key cryptography algorithms, namely ECC (Elliptic Curve Cryptography) and RSA (Rivets Shamir Adelman).

With the evolution and integration of information and communication technologies, the marine environment is being converted into a smart ocean of things. The only way to monitor the marine environment is to access marine information through satellites, radar, etc. Recently, many researchers have focused their interest on generating power from renewable energy. Among all the available renewable resources, ocean waves are attracting the interest of researchers for power generation. Therefore, this article focuses on designing a data-driven forecasting model for marine renewable energy generation applications. This article applies a novel Gini-impurity-index-based bidirectional long short-term memory model for selecting the best ocean/marine environmental factors to forecast wave height and ultimately predict power generation using the numerical model. This article presents short- and long-term forecasting results. In the experiment, four stations each are taken for both short- and long-term forecasting. The average root-mean-square error was approximately 0.17 for long-term forecasting and approximately 0.05 for short-term forecasting.