RETRACTED ARTICLE: Recommender system for home automation using IoT and artificial intelligence

Gladence, L. Mary; Anu, V. Maria; Rathna, R.; Brumancia, E.

doi:10.1007/s12652-020-01968-2

Cited by 63 publications

(28 citation statements)

References 9 publications

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“…A gadget free inhabitant action detecting framework utilizing Wi-Fi connections-empowered IOT gadgets for brilliant house. [19][20] Consider an on-street person on foot following framework over different moving cameras and in another article, built up a system for vehicle following and restriction dependent on 3-D compelled numerous pieces following. Quality-of contentdepends on joint one another to another source and channel coding framework for distinguishing people in a versatile observation cloud 4 .…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Security management in smart home environment

et al. 2021

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A Smart Home Environment (SHE) comprises various luxurious things which makes us very comfortable to live our lives happily and securely. The only problem that smart homes are facing is "SECURITY". Security for smart homes is the biggest task to achieve. For this purpose, this work is to build a product that provides the security for the smart homes automatically when the crime is going to happen. In general case, if any crime had taken place in any smart home, the general procedure of investigation will take place i.e. the people will complain to the police and the police will visit the place and after that he will observe the surroundings clearly. In order to pull up the clues if any, he will watch the CCTV footage, consult the nearby people in-order to draw some facts and then FIR will be filed.In order to get rid of this time-consuming process, the automatic crime detection is proposed. Here malware practices are identified, when a person attempts crime activity. This type of automatic process of detection of crime will ensure a complete security for the smart homes. We will track the CCTV camera pictures when the thief is trying to commit a crime and the information along with the pictures will be sent to the fog server and the fog server will analyze whether the person is doing crime or not. In case if a fog server had identified the person as a crime person or a thief then it automatically sends the information to the near-by police station and as well as owners of the house and then provides security for the smart homes.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Security management in smart home environment

et al. 2021

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“…Anything necessitates a wide range of technology that must be carefully integrated and orchestrated. These advancements in technology allow intelligent machines, machinery, appliances, and integrated automation systems [2,3] to automate routine operations and solve complex problems without human interference. Improvements in the smart workplace, smart data exploration, cognitive automation, and other aspects of business smartness should all be included.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

Awotunde

Chakraborty

Adeniyi

2021

Wireless Communications and Mobile Computing

127

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The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

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“…The application of image recognition has become more extensive due to the development of artificial intelligence in image processing models. Gladence et al [1] applied AIoT to home automation recommendations. In another work by Priya et al [2], the common image recognition CNN model was used in medical detection and prediction.…”

Section: Introductionmentioning

confidence: 99%

An AIoT Based Smart Agricultural System for Pests Detection

et al. 2020

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In this study, artificial intelligence and image recognition technologies are combined with environmental sensors and the Internet of Things (IoT) for pest identification. Real-time agricultural meteorology and pest identification systems on mobile applications are evaluated based on intelligent pest identification and environmental IoT data. We combined the current mature AIoT technology and deep learning and applied it to smart agriculture. We used deep learning YOLOv3 for image recognition to obtain the location of Tessaratoma papillosa and analyze the environmental information from weather stations through Long Short-Term Memory (LSTM) to predict the occurrence of pests. The experimental results showed that the pest identification accuracy reached 90%. Precise positioning can effectively reduce the amount of pesticides used and reduce pesticide damage to the soil. The current research provides the location of the pest and the extent of the pests to farmers can accurately use pesticide application at a precise time and place and thus reduce the agricultural workforce required for timely pest control, thus achieving the goal of smart agriculture. The proposed system notifies farmers of the presence of different pests before they start multiplying in large numbers. It improves overall agricultural economic value by providing appropriate pest control methods that decrease crop losses and reduce the environmental damage caused by the excessive usage of pesticides.

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RETRACTED ARTICLE: Recommender system for home automation using IoT and artificial intelligence

Cited by 63 publications

References 9 publications

RETRACTED ARTICLE: Security management in smart home environment

RETRACTED ARTICLE: Security management in smart home environment

Intrusion Detection in Industrial Internet of Things Network‐Based on Deep Learning Model with Rule‐Based Feature Selection

An AIoT Based Smart Agricultural System for Pests Detection

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