Neurocomputing for internet of things: object recognition and detection strategy

Modern and new integrated technologies have changed the traditional systems by using more advanced machine learning, artificial intelligence methods, new generation standards, and smart and intelligent devices. The new integrated networks like the Internet of Things (IoT) and 5G standards offer various benefits and services. However, these networks have suffered from multiple object detection, localization, and classification issues. Conventional Neural Networks (CNN) and their variants have been adopted for object detection, classification, and localization in IoT networks to create autonomous devices to make decisions and perform tasks without human intervention and helpful to learn in-depth features. Motivated by these facts, this paper investigates existing object detection and recognition techniques by using CNN models used in IoT networks. This paper presents a Conventional Neural Networks for 5G-Enabled Internet of Things Network (CNN-5GIoT) model for moving and static objects in IoT networks after a detailed comparison. The proposed model is evaluated with existing models to check the accuracy of real-time tracking. The proposed model is more efficient for real-time object detection and recognition than conventional methods

Семантическое распознавание информационных объектов на основе онтологического представления знаний о предметной области в задачах интеллектуального управления

В работе проанализированы особенности семантического распознавания информационных объектов в сведениях, доступных через Web. В качестве примеров рассматривается обнаружение устройств в Internet of Things, обнаружение Web-сервисов и поддержка информационной службы экстренного вызова. Для решения проблемы семантического распознавания в данной работе предложен переход на новый качественный уровень при обработке информации — использование обработки на семантическом уровне.У роботі проаналізовано особливості семантичного розпізнавання інформаційних об'єктів у відомостях, доступних через Web. У якості прикладів розглядається виявлення пристроїв в Іnternet of Things, виявлення Web-сервісів та підтримка інформаційної служби екстреного виклику. Для вирішення проблеми семантичного розпізнавання в цій роботі запропоновано перехід на новий якісний рівень при обробці інформації — використання обробки на семантичному рівні.The purpose of this work is to develop a conceptual approach to the construction of formal ontological model of information objects in the virtual information space of the Web and to create a technique of this model using for perception, recognition, interpretation and processing of these objects for the tasks of intelligent control. Information object is a representation that models an object from subject domain in the information space, which defines the structure, attributes, constraints, and perhaps behavior of the object

Low-Energy Convolutional Neural Networks (CNNs) using Hadamard Method

The growing demand for the internet of things (IoT) makes it necessary to implement computer vision tasks such as object recognition in low-power devices. Convolutional neural networks (CNNs) are a potential approach for object recognition and detection. However, the convolutional layer in CNN consumes significant energy compared to the fully connected layers. To mitigate this problem, a new approach based on the Hadamard transformation as an alternative to the convolution operation is demonstrated using two fundamental datasets, MNIST and CIFAR10. The mathematical expression of the Hadamard method shows the clear potential to save energy consumption compared to convolutional layers, which are helpful with BigData applications. In addition, to the test accuracy of the MNIST dataset, the Hadamard method performs similarly to the convolution method. In contrast, with the CIFAR10 dataset, test data accuracy is dropped (due to complex data and multiple channels) compared to the convolution method. Finally, the demonstrated method is helpful for other computer vision tasks when the kernel size is smaller than the input image size

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop