Weeds infestation causes damage to crops and limits the agricultural production. The traditional weeds controlling methods rely on agrochemicals which demand labour-intensive practices. Various methods are proposed for the pursuit of weeds detection using multispectral images. The machine vision-based weeds detection methods require the extraction of a large number of multispectral texture features which in turn increases the computational cost. Deep neural networks are used for pixel-based weeds classification, but a drawback of these deep neural network-based weeds detection methods is that they require a large size of images dataset for network training which is time-consuming and expensive to collect particularly for multispectral images. These methods also require a Graphics Processing Unit (GPU) based system because of having high computational cost. In this article, we propose a novel weeds detection model which addresses these issues, as it does not require any kind of supervised training using labelled images and multispectral texture features extraction. The proposed model can execute on a Central Processing Unit (CPU) based system as a result its computational cost reduces. The Predictive Coding/Biased Competition-Divisive Input Modulation (PC/BC-DIM) neural network is used to determine multispectral fused saliency map which is further used to predict salient crops and detect weeds. The proposed model has achieved 94.38% mean accuracy, 0.086 mean square error, and 0.291 root mean square error.

Short term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors such as time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations used for prediction, later the model improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR observed as 47% and it reduced to 8.9% for CFBP.

Artificial Intelligence (AI) is becoming a hot topic in the field of robotic research in the last few decades. Autonomous Unmanned Vehicles (AUV) are being used for different tasks like rescue, search, monitoring, aerial operations as well as underwater operations even AUV can aid where human reachability is impossible. Localization, tracking, and mapping are fundamentals of an autonomous system. The main problem of AUV, which attracts researchers, is the simultaneous localization and mapping, where no external positioning source is available like GPS. There are many proposed techniques and algorithms which can be used to solve this problem of AUV like GPS (Global Positioning System), Motion Capture System (MCS), Visual System, etc. with limitations. Some probabilistic solutions like Graph SLAM, EKF based SALM, and Fast SLAM are also available for this problem. EKF based SLAM is used for the non-linear model but has different issues (like inconsistency) when the map becomes large and complex. It does not work well with the non-Gaussian distribution. Fast SLAM algorithm is used with the non-Gaussian distribution. It can provide high speed computation and good accuracy but has issues during the resampling processes like particle depletion and degeneracy. On the other hand, Graph based SLAM can deal with large and complex maps and can process a large number of landmarks accurately and it can perform much better than EKF and Fast SLAM.

The AC power system is leading due to its established standards. The depleting thread of fossil fuels, the significant increase in cost and the alarming environmental situation raises concerns. An Islanded DC microgrid, due to its novel characteristics of being able to withstand faulty conditions, has increased the reliability, accuracy, ease of integration, and efficiency of the power system. Renewable energy sources, characteristically DC, have wide usability in a distributive network and, accordingly, less circuitry and conversion stages are required, eliminating the need of reactive power compensation and frequency sync. Constant power loads (CPLs) are the reason for instability in the DC microgrid. Various centralized stability techniques have been proposed in the literature; however, the grid system collapses if there is a fault. To compensate, an efficient distributive control architecture, i.e., droop control method is proposed in this research. The significant advantage of using the droop control technique includes easy implementation, high reliability and flexibility, a reduced circulating current, a decentralized control with local measurements, the absence of a communication link and, thus, it is economic. Moreover, it offers local control for each individual power source in the microgrid. To investigate the stability of the islanded DC microgrid with constant power loads using the droop control technique, a small signal model of the islanded DC microgrid was developed in MATLAB/Simulink. Simulations were carried out to show the efficiency of the proposed controller and analyze the stability of the power system with constant power loads.

Short-term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It is utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors like time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations were used for prediction, later the model was improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR was observed as 47% and it was reduced to 8.9% for CFBP.

For clinical study and diagnosis, compression of Electro Cardio Gram (ECG) signal is a fundamental step for processing. However, the compression and reconstruction introduce errors in the signal. Therefore, error minimization is crucial before using these signals for analysis and diagnosis. This paper presents an efficient method to minimize the reconstruction error using the adaptive filtering technique. Better reconstruction was achieved based on higher value of Compression Ratio and lesser value of Percent Root mean squared difference. Daubechies Wavelet easily detects the signal spikes while keeping less error rate using Least Mean Squared Error algorithm. However, the percentage value of error appeared to be minimum when using Daubechies Wavelet because of its small coefficients other than Haar and Coiflet Wavelet. Therefore, it was concluded that Daubechies Wavelet should have been used for error minimization in the reconstructed signal.