Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country’s economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general.

This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization algorithms, leverage biological behaviors for feature selection, they are limited by their inability to synergize diverse strategies, resulting in suboptimal performance and scalability. To address this, we introduce the Hybrid Predator Algorithm for Classification (HPA-C), a novel hybrid feature selection algorithm that uniquely integrates the framework of a nature-inspired feature selection technique with position update equations from other algorithms, harnessing diverse biological behaviors like echolocation, foraging, and collaborative hunting. Coupled with a custom convolutional neural network (CNN), HPA-C achieves superior classification accuracy (98.6–99.8%) on agricultural datasets (Plant Leaf Diseases, Weed Detection, Fruits-360, and Fresh n Rotten) and demonstrates exceptional adaptability across diverse imagery applications.

Studying critical currents, critical temperatures, and critical fields carries substantial importance in the field of superconductivity. In this work, we study critical currents in the current–voltage characteristics of a diluted-square lattice on an Nb film. Our measurements are based on a commercially available Physical Properties Measurement System, which may prove time consuming and costly for repeated measurements for a wide range of parameters. We therefore propose a technique based on artificial neural networks to facilitate extrapolation of these curves for unforeseen values of temperature and magnetic fields. We demonstrate that our proposed algorithm predicts the curves with an immaculate precision and minimal overhead, which may as well be adopted for prediction in other types of regular and diluted lattices. In addition, we present a detailed comparison between three artificial neural networks architectures with respect to their prediction efficiency, computation time, and number of iterations to converge to an optimal solution.

Contemplating the importance of studying current–voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks’ coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks’ initialization and coefficients’ generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current–voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

Energy consumption in residential, commercial and industrial buildings is one of the major contributors to global warming. Due to the increase in the latter, and growing global energy crisis, more attention is being paid to renewable energy resources (RES). The use of innovative concepts in existing buildings is gaining popularity to provide reduction in energy requirements for electricity, heating and cooling. In this paper, an electricity, heating and cooling cooperation mechanism among neighboring buildings with RES is proposed. It relies on adjusting the RES tariff with a mutual agreement between the neighboring buildings, with an aim to minimize the operational costs. For this purpose, a mathematical model is developed for joint energy cooperation, where surplus energy in one of the buildings is shared with others, thereby reducing dependency on the grid. The optimization structure of the environment friendly energy cooperation is nonlinear, which is linearized using the McCormick envelopes. A scenario for the city of Islamabad, Pakistan, is considered by utilizing its environmental data obtained from public domain websites. The simulation results show more than twenty percent energy cost savings with the proposed cooperation model.

Pipelines, in Reduced Instruction Set Computer (RISC) microprocessors, are expected to provide increased throughputs in most cases. However, there are a few instructions, and therefore entire assembly language codes, that execute faster and hazard-free without pipelines. It is usual for the compilers to generate codes from high level description that are more suitable for the underlying hardware to maintain symmetry with respect to performance; this, however, is not always guaranteed. Therefore, instead of trying to optimize the description to suit the processor design, we try to determine the more suitable processor variant for the given code during compile time, and dynamically reconfigure the system accordingly. In doing so, however, we first need to classify each code according to its suitability to a different processor variant. The latter, in turn, gives us confidence in performance symmetry against various types of codes—this is the primary contribution of the proposed work. We first develop mathematical performance models of three conventional microprocessor designs, and propose a symmetry-improving nonlinear optimization method to achieve code-to-design mapping. Our analysis is based on four different architectures and 324,000 different assembly language codes, each with between 10 and 1000 instructions with different percentages of commonly seen instruction types. Our results suggest that in the sub-micron era, where execution time of each instruction is merely in a few nanoseconds, codes accumulating as low as 5% (or above) hazard causing instructions execute more swiftly on processors without pipelines.