MDS code is a linear code that achieves equality in the Singleton bound, and projective MDS (PG-MDS) is MDS code with independents property of any two columns of its generator matrix.   In this paper, elementary methods for modifying a PG-MDS code of dimensions 2, 3, as extending and lengthening, in order to find new incomplete PG-MDS codes have been used over . Also, two complete PG-MDS codes over  of length  and 28 have been found.

An essential component of food production is nitrogen (N). Crops waste about half of the nitrogen fertilizer input, which is then released into the atmosphere as gas emissions or polluting of aquatic groups. It is necessary to reach manufacture stages that promote world food security in the absence of compromising quality. safety of the environment. It is estimated that by 2050, N pollution levels will have risen 150% from 2010 levels, with the The agricultural sector is responsible for 60% of this growth. In this review fertilizer Nitrogen should take into account while discussing air pollution caused by gases that contain nitrogen, which might result in issues like the greenhouse effect . Nopoint pollution, the involvement of farmer manag

An essential component of food production is nitrogen (N). Crops waste about half of the nitrogen fertilizer input, which is then released into the atmosphere as gas emissions or polluting of aquatic groups. It is necessary to reach manufacture stages that promote world food security in the absence of compromising quality. safety of the environment. It is estimated that by 2050, N pollution levels will have risen 150% from 2010 levels, with the The agricultural sector is responsible for 60% of this growth. In this review fertilizer Nitrogen should take into account while discussing air pollution caused by gases that contain nitrogen, which might result in issues like the greenhouse effect. Nopoint pollution, the involvement of farme

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par