A survey on internet of vehicles: Applications, security issues & solutions

The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.

/pdf/deep-learning-based-leaf-disease-detection-in-crops-using-1iq6v39n.pdf

Deep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications

The Electrocardiogram (ECG) arrhythmia classification has become an interesting research area for researchers and developers as it plays a vital role in early prevention and diagnosis of cardiovascular diseases. In ECG signal classification, the feature extraction and selection processes are critical steps. Thus, in this paper, different ECG signal descriptors based on one-dimensional local binary pattern (LBP), wavelet, higher-order statistical (HOS), and morphological information are introduced for feature extraction. For feature selection and classification processes, a new hybrid ECG arrhythmia classification approach called MRFO-SVM that combines a metaheuristic algorithm termed Manta ray foraging optimization (MRFO) with support vector machine (SVM) is proposed to automatically determine the relevance features of LBP, HOS, wavelet and magnitude values. In MRFO-SVM approach, the MRFO is utilized to optimize the parameters of SVM and to select the significant features subset that provides the best classification performance, meanwhile SVM is used for classification purposes. The proposed MRFO-SVM approach is trained on the MIT-BIH Arrhythmia database containing four abnormal and one normal heartbeats. The experimental results of ECG arrhythmia classification using the proposed MRFO-SVM revealed with evidence its superiority with overall classification accuracy of 98.26% over seven well-known metaheuristic algorithms.

An efficient ECG arrhythmia classification method based on Manta ray foraging optimization

Apple leaf disease recognition method with improved residual network

Internet of Vehicles (IoV) is treated as an extension of Vehicle-to-Vehicle (V2V) communication network. IoV helps in enhancing driving aids with the help of vehicle Artificial Intelligence (AI) awareness of other vehicles and their actions. IoV is connected in an adhoc networking environment which utilizes each vehicle in the network as a node, called Vehicular Ad Hoc Network (VANET), where the vehicles may be also connected to the public Internet. It is specifically important for the autonomous vehicles because they can instantaneously communicate with other vehicles surrounding them. In addition, safely avoiding accident prone zones is crucial in order to continue secure and smart transportation. Since the communication among various entities involved in the IoV environment is via open channel, it gives an opportunity to a passive/active adversary to intercept, modify, delete or even insert fake information during communication. It is then a serious concern for the vehicles users to determine whether the received information is genuine. In this survey paper, various security aspects, threats and attacks, network and threat models related to the IoV environment are discussed. Next, a taxonomy of security protocols is given that is essential to provide IoV data security. In particular, focus on various authentication protocols is given that is needed for mutual authentication among the involved entities in the IoV environment for secure communication. A detailed comparative analysis among various state-of-art authentication protocols proposed in the related IoV environment is provided to show their effectiveness as well as security and functionality features. Moreover, some testbeds are described that were designed and implemented for the IoV environment. In addition, some future challenges for IoV security protocols are also highlighted that are necessary to address in the future.

Authentication Protocols in Internet of Vehicles: Taxonomy, Analysis, and Challenges

Identifying and classifying plant disease using resilient LF-CNN

This work aims to provide a survey on internet of things (IoT), internet of vehicles (IoV) and internet of everything (IoE). The internet of things (IoT) provides interconnection between various physical devices such as sensors devices, mobile phones, laptop, PDA and so on. Nowadays, IoT also enables connection between vehicles, buildings and other items that are fixed with sensors, actuators and gateways. Internet of vehicles (IoV) is identified from the internet of things (IoT). Internet of vehicles (IoV) is used to make an interconnection between the things, vehicles and environments to transfer the data and information between the networks. Internet of everything is an enhanced version of internet-based technologies such as internet of things, internet of humans and internet of digital. IoE provides end to end connectivity among procedures, knowledge and ideas engaged across all connectivity use cases. This paper discusses various challenges and issues in modern IoT, IoV, and IoE system. In addition, this paper also discusses security issues and various application of IoT in healthcare. Though IoT devices are used in modern applications with good performance, however, some challenges still exist. In order to overcome these issues, various open research problems are identified in this paper.

A survey on internet of vehicles: applications, technologies, challenges and opportunities

Digital data is growing at immense rates day by day and finding efficient storage and security mechanisms is a challenge. Cloud storage has already gained popularity because of the huge data storage capacity in storage servers made available to users by the cloud service providers. When lots of users upload data in cloud there can be much redundant data as well and this can waste storage space as well as affect transmission bandwidth. To promise efficient storage handling of this redundant data is very important which is done by the concept of deduplication. The major challenge for deduplication is that most users upload data in encrypted form for privacy and security of data. There are many prevailing mechanisms for deduplication some of which handle encrypted data as well. The purpose of this paper is to conduct a survey of the existing deduplication mechanisms in cloud storage and to analyse the methodologies used by each of them.

A study on data de-duplication schemes in cloud storage

The aim of this paper is to analyze the performance of an intrusion detection model using single- and ensemble-based classifiers. Several tree-based single classifiers were analyzed. The ensemble of tree-based classifiers was also analyzed to differentiate the superiority in their performance. Different proportions of the benchmark KDD dataset are utilized for observing the performance of the model. Classification based on the accuracy, model building time, and kappa statistic is evaluated as the performance measures in this paper. The base and ensemble classifiers resulted in better accuracy are observed in the experiments and only Naive Bayes and random tree resulted in minimum model building time. Most of the classifiers produced better results for kappa statistic. The highest statistic is computed for ADA classifier, and lowest error is computed for the random forest ensemble.

Performance Analysis of Single- and Ensemble-Based Classifiers for Intrusion Detection.

Clustering of plant disease from digital images is an arduous task due to its dynamic nature and change of appearance under different environmental conditions. In most cases, the image captured in the real-time scenario is subjected to added noise, distortion, poor lighting conditions, and other potential factors that results in poor model performance during the process of discriminating between normal and disease-affected samples. It eventually maximizes the margin of the error rate, thereby leading to misclassification of disease of different varieties of plants in the database with other categories. This paper presents an effective deep clustering-based plant disease categorization algorithm, Boosted-Deep Embedded Regularized Clustering (DEPICT). This model integrates the convolutional autoencoder model with locality-preserving constraints and group sparsity into the network, which improves the embedded learning representation of the images. The PlantVillage and PDD image databases are accessed to develop this model for maize crop. The images are segmented by eliminating the background, cropped, augmented before model training. The performance of the system is evaluated by clustering accuracy and normalized mutual information. The proposed Boosted-DEPICT exhibits better performance, attains promising results with an accuracy of 97.73% and 91.25% on PV and PDD datasets, and outperforms state-of-the-art deep clustering algorithms. This system could be further enhanced by automating the entire process and transforming it into a mobile application for real-time analysis to gain instant results from any region.

Boosted-DEPICT: an effective maize disease categorization framework using deep clustering

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