Because textile industries are intensely water-consuming and generate a huge quantity of wastewater, the present study examines the scope of using solar thermal technology to treat wastewater from textile industries. A hybrid technology, comprising a compound parabolic concentrator-based solar thermal system in conjunction with a Membrane Distillation (MD) system, is experimented with for wastewater treatment in textile industries. The MD system requires a water temperature of around 90 °C for efficient functioning. The advanced MD technology using waste heat combined with solar heat to meet the system’s thermal load is technologically evaluated for an experimental textile industry in India. Moreover, the present study critically analyses the techno economics of the proposed hybrid technology. A detailed financial analysis has revealed that, besides technological superiority, the recommended technology is also financially rewarding for wastewater treatment in the textile industry. To cope with the delayed payback period, financial incentives are recommended so that the system becomes a lucrative technological option.

Source separation is important task in intelligibility for human as well as speech recognition system. The command from speech is required to be recognized for voice operated Internet of Things devices. The speech separation is complex task when number of noise sources increase with respect to room acoustics. The mixing of sounds and their separation is experimentation approach considered. The feature extraction is performed using 1D CNN model. The very less complex CNN model provides various in depth features which can be easily separated in the classifier. The model is proposed using recurrent neural network (RNN). The performance is evaluated and compared with other machine learning classifiers for classification of commands. The accuracy of more than 98% is seen with the use of proposed RNN model with combination of gated recurrent unit (GRU) and long short term memory (LSTM).

This project intends to create and implement an IoTbased smart farm protection system. Farm crops are frequently damaged by local animals and birds, and there is also the risk of fire destroying crops. Farmers may suffer significant losses as a result. Because farmers are unable to watch and secure their farms 24 hours a day, this technology can assist them in detecting threats and taking appropriate action. The Arduino Uno is the heart of this system. [A motion sensor is used to detect animals approaching the farm, and a smoke sensor is used to detect the fire. If the motion sensor detects an approaching animal, a buzzer is activated to produce a noise. At the same moment, the farmer is notified. The system includes a GSM module that sends SMS to the farmer about the situation so that he may analyses it himself. In the event that crops catch fire, the smoke sensor will detect the smoke and activate an automated water sprinkler system to put out the fire. There is also a camera for live farm monitoring. This technology guarantees that farms are completely protected from animals and fire, preventing losses for farmers.

Objectives: To fix the security issues of devices in internet of things (IOT) systems that arise when Machine learning (ML) and Deep Learning algorithm (DLA) are implemented in the IOT systems. Methods: Each packet of IoT threats has been filtered by using suitable attack model for primary attack detection. In deep learning, network traffic field’s information is extracted from of packet and is used as the training features. Attack is proposed to be detected by the use of database-based features of attack. If attack is found then packet will be discarded along with update feedback to filtering stage for primary attack detection. Knowledge Discovery and Data Mining (KDD) dataset is used in this study. Different types of Mirai attack are considered for classification. The performance of the proposed method has been compared in terms of accuracy and execution time with earlier work using traditional ML-DL methods. Findings: The proposed technique is seen to achieve high accuracy level at the least computational time, concurrently with much higher recall and G-mean values Several algorithms are used to secure the IOT devices from various types of threats and a comparison is depicted with their accuracy and execution time. Results show that the proposed methodology using Convolution neural network (CNN) for classification, can achieve the accuracy ~0.9976 with execution time1.30 sec and G-mean 0.7865 only. Novelty: The proposed method is essentially a judiciously configured ML- DL technique which is novel and exhibits better performance in terms of mitigating IoT threats. Keywords: IOT devices and threats; Machine Learning; Deep Learning; KDD; Security and privacy