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Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions. [ABSTRACT FROM AUTHOR]

A thriving agricultural system is the cornerstone of an expanding economy of agricultural countries. Farmers' crop productivity is significantly reduced when they choose the crop without considering environmental factors and soil characteristics. Crop prediction enables farmers to select crops that maximize crop yield and earnings. Accurate crop prediction is mainly concerned with agricultural research, which plays a major role in selecting accurate crops based on environmental factors and soil characteristics. Recently, recommender systems (RS) have gained much attention and are being utilized in various fields such as e-commerce, music, health, text, movies etc. Machine learning techniques can help predict the crop accurately. We proposed an innovative artificial neural network (ANN) based crop prediction system (CPS) to address the farmer's issue. The parameters considered during sensor-based soil data collection for this study are nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, electrical conductivity, and soil texture. Python programming language is used to design and validate the proposed system. The accuracy and reliability of the proposed CPS are assessed by using accuracy, precision, recall, and F1-score. We also optimized the proposed CPS by performing a hyperparameter Optimization analysis of applied learning methods. The proposed CPS model accuracy for both real-time collected and state-of-the-art datasets is 99%. The experimental results show that our proposed solution assists farmers in selecting the accurate crop and producing at their best, increasing their profit. [ABSTRACT FROM AUTHOR]