The branch of research known as “sentiment analysis and opinion mining” focuses on extracting meaning from the written words of others by studying their thoughts, feelings, judgments, and attitudes. Natural language processing is one of the most active study fields in data mining, web mining, and text mining. There are numerous uses of sentiment analysis, such as assessing the impact of events on social networks and gauging public opinion on products and services. In the same way as blogs, forums, microblogs, and social networks such as Twitter and Facebook have grown in popularity, sentiment analysis is becoming increasingly important. It is possible to measure sentiments that are captured in digital form using supervised machine learning and lexical-based techniques.

Data Extraction Approach using Natural Language Processing for Sentiment Analysis

Water is one of the most important elements of environment and essential for all living objects in planet earth. According to United State geological survey about 1.7 trillion of water are wasted ever year over world. A family can waste 9400 gallons of water every year. Water is also wasted when water pump runs even after water tank is full. Alongside this there is only 1% of drinking water available where water marks up around 71% of the earth surface and availability of drinking water is subsiding every year. It’s now a duty of every people on the earth to save water and ensure supply of pure drinking water. This paper introduces an IoT SMS enabled gadget that monitor water level and switches motor automatically when water goes above or below a certain level. This device also provides information about pH level of water additionally. All the stated information is sent to user through SMS.

Intelligent Water Level Monitoring and Water Pump Controlling System using IOT

Using deep learning and a behavioural approach, this study presents a real-time detection and monitoring system for tired drivers. The objective is to develop and build software that collects real-time driver behaviour while driving and trains it using convolutional neural networks (CNNs) to anticipate the driver's behaviour. An intelligent video-based gadget, a dataset of drowsy drivers, and CNN architecture were used to achieve this goal. MATLAB and a deep learning technology were used to implement the concepts. Tests revealed that the system has a 99.8% accuracy rate for detecting anomalies. A prototype model of the system was created using MATLAB.

Deep Learning based Drowsiness Detection and Monitoring using Behavioural Approach

In the current climate, global warming is a threat to society. As a result of burning fossil fuels in automobiles, carbon dioxide is released into the atmosphere, which adds to environmental pollution. One of the more constructive responses to this dilemma is the introduction of hybrid vehicles. Electric, solar, and IC engines all work together in a hybrid vehicle's transmission system. Solar power is used with a normal internal combustion engine in this method. Consequently, it is known as a "Hybrid Solar Car" (HSV). Both internal combustion engines and solar energy with an electric motor may be used to move it. Solar-powered automobiles do not produce any pollution in the real world. Development and introduction of hybrid electric vehicles are under underway. In order to travel long distances, these cars need to recharge their batteries often. This leads in an increase in the cost of electricity. Hybrid solar cars, which also contribute to a greener environment, will help to tackle these concerns.

Deployment of Slow power Hybrid Electric Vehicle based on Combustion Engine

Every day, a large number of people die in road accidents around the world. The use of a smart helmet band is an effective solution to the problem. The smart helmet band is an innovative concept that makes motorcycle riding safer than ever before. By utilizing GPS technology, the smart helmet band can assist in the event of an accident. There are basically 3 sections into which the system is spilt, a helmet circuit, a mobile application and car circuits. The helmet circuit features an IR and alcohol detection sensor at first. Road accidents are fast increasing in developing countries like India, as transportation technology advances and the total number of cars increases. This technological innovation also exacerbated traffic hazards. Two-wheelers are responsible for 25% of all traffic fatalities. As a result, traffic accidents become more common, resulting in severe loss of life due to insufficient emergency assistance. If these two conditions are not met, the Smart Helmet part of the preventative prevents the relay from turning on the engine. The microprocessor controls the function of the relay, and as a result, the ignition.

Smart Helmet using IoT for Alcohol Detection and Location Detection System

Hate speech spreads as the quantity of content on the internet grows. Automated methods for identifying hate speech in text face a number of challenges, which we investigate and assess. Language complexity, differing views on what constitutes hate speech, and data availability restrictions for algorithm training and testing are some of the difficulties. As a result, it may be difficult to decipher the reasoning behind the decisions made by many current methods. With our multiview SVM technique, we give near-state of the art SVM results that are easier to comprehend than neural approaches. In addition, we look at the challenges that this endeavour faces on a technological and practical level.

Challenges for Hate Speech Recognition System: Approach based on Solution

Facial analysis in videos and images has been a relatively tough task for machine learning models. Recent use of deep learning approaches has demonstrated substantial improvement in results and reliability and can be used for problems such as face recognition, emotion recognition and emotion reaction prediction. In the case of emotion reaction, relevant information of emotions in individual frames often must be aggregated over a variable length sequence of frames and speech signal to produce an appreciable prediction. Emotion reaction prediction is a subset of sequence analysis task and heavily relies on dynamic temporal and spectral features. Convolution neural networks (CNNs) have been extensively used for emotion recognition problems and have produced reliable results. However, they lack the ability to extract time-series information from a sequence of inputs and cannot model an emotion transaction. Recurrent neural networks (RNNs) are being used profoundly due to their ability to yield impressive results on a variety of tasks in the field of sequence analysis. In this work, we propose a system for emotion recognition and reaction prediction in videos. The primary focus is experimental analysis of a hybrid CNN-RNN architecture for emotion transaction analysis that can recognize the emotion in a frame in a video and predict its appropriate reaction.

Ashish Pawar

Papers

Challenges for Hate Speech Recognition System: Approach based on Solution

Emotion recognition and reaction prediction in videos