-- A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a sugges ted way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety an d operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be sol ved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering . I. INTRODUCTION Because of rapid advancements in technology, engineering industries have changed a lot.

In this draft we consider the problem of forecasting rainfall across India during the four monsoon months, one day as well as three days in advance. We train neural networks using historical daily gridded precipitation data for India obtained from IMD for the time period $1901- 2022$, at a spatial resolution of $1^{\circ} \times 1^{\circ}$. This is compared with the numerical weather prediction (NWP) forecasts obtained from NCEP (National Centre for Environmental Prediction) available for the period 2011-2022. We conduct a detailed country wide analysis and separately analyze some of the most populated cities in India. Our conclusion is that forecasts obtained by applying deep learning to historical rainfall data are more accurate compared to NWP forecasts as well as predictions based on persistence. On average, compared to our predictions, forecasts from NCEP-NWP model have about 34% higher error for a single day prediction, and over 68% higher error for a three day prediction. Similarly, persistence estimates report a 29% higher error in a single day forecast, and over 54% error in a three day forecast. We further observe that data up to 20 days in the past is useful in reducing errors of one and three day forecasts, when a transformer based learning architecture, and to a lesser extent when an LSTM is used. A key conclusion suggested by our preliminary analysis is that NWP forecasts can be substantially improved upon through more and diverse data relevant to monsoon prediction combined with carefully selected neural network architecture.

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods.

This comprehensive review systematically evaluates Machine Learning (ML) methodologies employed in the detection, prediction, and analysis of mental stress and its consequent mental disorders (MDs). Utilizing a rigorous scoping review process, the investigation delves into the latest ML algorithms, preprocessing techniques, and data types employed in the context of stress and stress-related MDs. The findings highlight that Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models consistently exhibit superior accuracy and robustness among all machine learning algorithms examined. Furthermore, the review underscores that physiological parameters, such as heart rate measurements and skin response, are prevalently used as stress predictors in ML algorithms. This is attributed to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. Additionally, the application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. The synthesis of this review identifies significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for detection and prediction of stress and stress-related MDs.

Text Summarization is highly useful in today's digital world. I will now walk you through some important methods to implement Text Summarization. This is the traditional method, in which the process is to identify significant phrases/sentences of the text corpus and include them in the summary. The summary obtained from this method will contain the key-sentences of the original text corpus. It can be done through many methods, I will show you using gensim and spacy.