Showing papers in "Sustainable Computing: Informatics and Systems in 2020"

TL;DR: A simplified CNN model is proposed comprising of 8 hidden layers that performs better than the traditional machine learning approaches as well as pretrained models and performs extremely well on dataset other than PlantVillage.

TL;DR: This paper introduces a novel exponential spider monkey optimization which is employed to fix the significant features from high dimensional set of features generated by SPAM and demonstrates that the selected features by Exponential SMO effectively increase the classification reliability of the classifier in comparison to the considered feature selection approaches.

TL;DR: This research uses innovative platform technology to be applied to the cloud agriculture platform, allowing farmland with limited network information resources to be integrated and automated, including agricultural monitoring automation, pest management image analysis and monitoring, which can be used to solve the predicament of large-area automation construction.

TL;DR: A Multi-Objective Based Clustering and Sailfish Optimizer guided routing method to sustain energy efficiency in WSNs and shows significantly better results than other optimization-based approaches.

TL;DR: An Internet of Things (IoT) based system by designing a novel Nitrogen-Phosphorus-Potassium (NPK) sensor with Light Dependent Resistor (LDR) and Light Emitting Diodes (LED) to monitor and analyze the nutrients present in the soil is presented.

TL;DR: This paper presents a comprehensive review of the work done until now in the field of power quality disturbance detection and classification and different combinations of signal processing techniques with machine learning techniques have been reviewed.

TL;DR: This work uses a low-cost sensorized platform, capable of monitoring meteorological phenomena following the Internet of Things paradigm, with the goal to apply an alert disease model on the cultivation of the vine.

TL;DR: An automatic disease detection and classification method in radish fields is proposed by using a camera attached to an unmanned aerial vehicle (UAV) to capture high quality images from the fields and analyze them by extracting both color and texture features, then used K-means clustering to filter radish regions.

TL;DR: An Energy Efficient Particle Swarm Optimization (PSO) based Clustering (EEPSOC) technique for the effective selection of cluster heads (CHs) among diverse IoT devices and an artificial neural network (ANN) based classification model is applied.

TL;DR: The simulation results prove that the proposed hybrid meta-heuristic algorithm outperforms against state-of-the-art ACO-based, well-known heuristic-based FFD algorithms, and random-based approach in terms of total power consumption, resource wastage, the total data transfer rate in network, and number of active servers in use.

TL;DR: This paper suggests a method to divide the network into some cells in a geographic way and applies two mobile sinks to gather the data sensed by these cell nodes and shows that applying EGRPM results in a significant decrease in average energy consumption and data delivery delay and causes a substantial increase in packet delivery rate and network lifetime.

TL;DR: A review of 45 deep learning-based techniques recently proposed for 33 different crops using 14 famous convolutional neural network architectures for leaf stress identification in vegetables, fruits and other crops is presented.

TL;DR: This paper proposed an energy efficient independent task scheduler using supervised neural networks with the aim to reduce makespan, energy consumption, execution overhead and number of active racks and results clearly indicate that proposed work outperforms considered algorithms.

TL;DR: The results have shown that leaf reflections can be successfully used in disease detection of pepper disease detection.

TL;DR: This paper introduces a novel approach for the automatic detection and classification of plant leaf diseases, based on the image process techniques, which can identify whether the plant is the diseased plant or not.

TL;DR: The distinctive nature of SDA and its complexity over the SG data are discussed, and a detailed taxonomy abstracted into a novel process model is presented, which highlights various research challenges such as secure data collection and preprocessing, secure load data processing and storage, load prediction, load management and analysis, data security and privacy issues, and data communication.

TL;DR: A modified Flower Pollination Algorithm has been employed to train Artificial Neural Network to predict soil moisture quantity and the proposed method is compared with well known PSO supported ANN and Cuckoo Search supported ANN along with MLP-FFN classifier.

TL;DR: A state of the art ecosystem for a portable load forecasting device is proposed by means of low-cost, open-source hardware that is experimentally found to be functioning with a high degree of accuracy and impressive performance is demonstrated by some specific machine learning models, considered to be suitable for the proposed framework.

TL;DR: An Energy-aware Grid-based Data Aggregation Scheme in Routing (EGDAS-RPL) protocol for AIoT, which reduces the packet loss ratio and end-to-end delay and increases the lifespan of the network.

TL;DR: The investigations indicate that the green goal should certainly be addressed in the overall cycle of GIoT, including design, production, utilization, and ultimately disposal/recycling.

TL;DR: The obtained results indicate that NAR dynamic neural network has a better performance in comparison with FFNN and NARX models, and these results are critical for water resources management and planning particularly in the field of dam reservoir operation.

TL;DR: A hybrid approach which involves the blending of Differential Evolution, Genetic Algorithm, and Whale Optimization aim at solving mixed-integer optimization problem known as Unit Commitment scheduling problem and has given promising results.

TL;DR: The results showed that wheat planting date could be determined successfully from climate information obtained in the first 300 days with the help of machine learning techniques combined with feature selection using genetic algorithm, which will prevent low productivity, financial and labor loss as a result of inaccurate planting date.

TL;DR: The paper puts forward the scientific, feasible and effective policy measures with Shandong characteristics to promote the sustainable development of low-carbon economy.

TL;DR: A carbon emission model is established and the relationship between the carbon emissions of residential building materials in different regions is: severe cold area, mild area, hot summer and cold winter area, and the whole life cycle of buildings.

TL;DR: New energy-efficient and secure clustering based data transmission in pervasive wireless networks using red deer algorithm (RDA) based clustering technique with blockchain enabled secured data transmission, named as RDAC-BC is presented.

TL;DR: This study integrated application of green sustainable development theory, enterprise theory and enterprise strategic innovation theory, combining the particularity of manufacturing enterprise, to guide manufacturing enterprises how to abandon the traditional energy-intensive, highly polluting manufacturing enterprise management mode.

TL;DR: Results shows improvement in the feasibility and performance of CNN over other machine learning models, and high performance shows the immediate crop disease identification ability of deep learning techniques over the different feature extraction models.

TL;DR: Temperature adaptive intelligent sleep-scheduling strategy (RL-Sleep) for the wireless sensor nodes has been proposed based on Reinforcement Learning which enables a node in the network to perceive the environment and decide autonomously about the action conducive for a stable operation of the network.

TL;DR: The results show that regimes form a certain “blocking” for the marketing of NEV, and the “ blocking” greatly suppresses the growth of the market share, and application policies should be made with notice of regional disequilibrium to improve the market competitiveness of NEVs and ensure synchronous and sustainable growth of sales and market share.