Plant leaf disease classification using EfficientNet deep learning model

Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT.

Determining how to structure vehicular network environments can be done in various ways. Here, we highlight vehicle networks' evolution from vehicular ad-hoc networks (VANET) to the internet of vehicles (IoVs), listing their benefits and limitations. We also highlight the reasons in adopting wireless technologies, in particular, IEEE 802.11p and 5G vehicle-to-everything, as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments. We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems' requirements. The presentation of each paradigm is given from a historical and logical standpoint. In particular, vehicular fog computing improves on the deficiences of vehicular cloud computing, so both are not exclusive from the application point of view. We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks, showing that they complement each other and share problems and limitations. As these networks still have many opportunities to grow in both concept and application, we finally discuss concepts and technologies that we believe are beneficial. Throughout this work, we emphasize the crucial role of these concepts for the well-being of humanity.

Computing Paradigms in Emerging Vehicular Environments: A Review

The monitoring and control of crops in precision agriculture sometimes requires a high collection frequency of information (e.g., temperature, humidity, and salinity) due to the variability in crops. Data acquisition and transmission are generally achieved thanks to wireless sensor networks. However, sensor nodes have limited resources. Thus, it is necessary to adapt the increase in sampling frequency for different crops, under application constraints (reliability, packet delay, and lifetime duration). In this paper, we propose to properly tune IEEE 802.15.4 MAC parameters ( macMinBE and macMaxCSMABackoffs ) and the sampling frequency of deployed sensor nodes. An analytical model of network performance is derived and used to perform the tuning of these tradeoff parameters. Simulation analysis shows that our scheme provides an efficient increase of sampling frequency of sensor nodes while satisfying application requirements.

Performance Management of IEEE 802.15.4 Wireless Sensor Network for Precision Agriculture

Energy efficiency is one of the major issues in wireless sensor networks (WSNs) and their applications. Distributed techniques with low message and time complexities are expected in WSNs. Connected dominating sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. Most of the existing CDS approaches suffer from a very poor approximation ratio, high time, and message complexities. This paper proposes two novel approaches for CDS distributed construction in WSNs. The proposed approaches are intended to construct a small CDS as well as allowing energy-efficient CDS construction and maintenance in WSNs. Simulation shows that our distributed approaches have an approximation factor of 7.5 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms.

https://journals.sagepub.com/doi/pdf/10.1155/2014/625061

Distributed Energy-Efficient Approaches for Connected Dominating Set Construction in Wireless Sensor Networks

Semi-supervised Selective Clustering Ensemble based on constraint information

With the fast development of various computing paradigms, the amount of data is rapidly increasing that brings the huge storage overhead. However, the existing data deduplication techniques do not ...

A feature-based intelligent deduplication compression system with extreme resemblance detection

The performance of differential evolution (DE) algorithm highly depends on the selection of mutation strategy. However, there are six commonly used mutation strategies in DE. Therefore, it is a challenging task to choose an appropriate mutation strategy for a specific optimization problem. For a better tackle this problem, in this paper, a novel DE algorithm based on local fitness landscape called LFLDE is proposed, in which the local fitness landscape information of the problem is investigated to guide the selection of the mutation strategy for each given problem at each generation. In addition, a novel control parameter adaptive mechanism is used to improve the proposed algorithm. In the experiments, a total of 29 test functions originated from CEC2017 single-objective test function suite which are utilized to evaluate the performance of the proposed algorithm.
 The Wilcoxon rank-sum test and Friedman rank test results reveal that the performance of the proposed algorithm is better than the other five representative DE algorithms.

A novel mutation strategy selection mechanism for differential evolution based on local fitness landscape

Most current automatic summarization methods are for English texts. The distinction between words in Chinese text is large, the types of parts of speech are many and complex, and polysemy or ambiguous words appear frequently. Therefore, compared with English text, Chinese text is more difficult to extract useful feature words. Due to the complex syntax of Chinese, there are currently relatively few automatic summarization methods for Chinese text. In the past, only the important sentences in the original text can be selected and simply arranged to obtain a summary with chaotic sentences and insufficient coherence. Meanwhile, because Chinese short text usually contains more redundant information and the sentence structure is not neat, we propose a topic-based automatic summary method for Chinese short text. Firstly, a key sentence selection method is proposed combining topic words and TF-IDF to obtain the score of each text corresponding to the topic in the original text data. Then the sentence with the highest score as the topic sentence of the topic is selected. Considering that the short text of Weibo may contain a lot of irrelevant information and sometimes even lack some important components of topic, three retouching mechanisms are proposed to improve the conciseness, richness and readability of topic sentence extraction results. We validate our approach on natural disaster and social hot event datasets from Sina Weibo. The experimental results show that the polished topic summary not only reflects the exact relationship between topic sentences and natural disasters or social hot events, but also has rich semantic information. More importantly, we can almost grasp the basic elements of natural disaster or social hot event from the topic sentence, so as to help the government guide disaster relief or meet the needs of users for quickly obtaining information of social hot events.

Najla Al-Nabhan

Papers

Distributed Energy-Efficient Approaches for Connected Dominating Set Construction in Wireless Sensor Networks

Semi-supervised Selective Clustering Ensemble based on constraint information

A feature-based intelligent deduplication compression system with extreme resemblance detection

A novel mutation strategy selection mechanism for differential evolution based on local fitness landscape

Topic-based automatic summarization algorithm for Chinese short text