Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large ...margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.
Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in smart city. Recently, the research community in the field of IoT Security endeavor hard to build ...anomaly, intrusion and cyber attacks traffic identification model using Machine Learning (ML) algorithms for IoT security analysis. However, the critical and significant problem still not studied in depth that is how to select an effective ML algorithm when there are numbers of ML algorithms for cyber attacks detection system for IoT security. In this paper, we proposed a new framework model and a hybrid algorithm to solve this problem. Firstly BoT-IoT identification dataset is applied and its 44 effective features are selected from a number of features for the machine learning algorithm. Then five effective machine learning algorithm is selected for the identification of malicious and anomaly traffic identification and also select the most widely ML algorithm performance evaluation metrics. To find out which ML algorithm is effective and should be used to select for IoT anomaly and intrusion traffic identification, a bijective soft set approach and its algorithm is applied. Then we applied the proposed algorithm based on bijective soft set approach. Our experimental results show that the proposed model with the algorithm is effective for the selection ML algorithm out of numbers of ML algorithms.
•This paper investigate Machine Learning (ML) algorithm and effective features.•This paper proposed a new framework model and a hybrid algorithm.•The basic technique used in this paper is bijective soft set and proposed new algorithm.•This paper selected effective ML algorithm and features for the identification attacks.•Finally, the paper validates the selected ML algorithm, Feature set for Attacks traffic.
•This paper study Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey.•This survey paper describes the significant literature survey of Sustainable ...Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification.•In this paper, most cited methods and datasets of features were identified, read and summarized.•In this paper, different classification techniques for SSC network traffic classification are presented.•In this paper, in the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.
This survey paper describes the significant literature survey of Sustainable Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification. Considering relevance and most cited methods and datasets of features were identified, read and summarized. As data and data features are essential in Internet traffic classification using machine learning techniques, some well-known and most used datasets with details statistical features are described. Different classification techniques for SSC network traffic classification are presented with more information. The complexity of data set, features extraction and machine learning methods are addressed. In the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.
Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this ...purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.
The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, ...a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.
The energy Internet (EI)-based vehicle-to-grid (V2G) technology facilitates the electric vehicles not only to distribute additional electricity into grid systems, but also support receiving back from ...the power grid in the form of charging. The secure key establishment is quite significant to initiate the bidirectional electricity power delivery into and from the system. To effectively implement any EI-based V2G communication, the authentication protocol must be free from cyber attacks. In this article, we not only explore the drawbacks of several smart grid-based authentication protocols but also bring forth the limitations of a recently presented EI-based V2G scheme by Gope and Sikdar. The examined drawbacks in this protocol may disrupt its proper functioning, since it faces desynchronization problems while logging into the mobile device bearing registration parameters. The scheme is also vulnerable to replay attack and man-in-the-middle attack. The user is also unable to validate session key in the protocol. Considering these limitations, we propose a novel and efficient V2G protocol framework enabling the vehicles to communicate or recharge at desired recharging stations. The results of the proposed framework are compared with several contemporary schemes, and its security features are validated by random oracle model-based formal analysis.
South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate ...the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens' relief since in our daily lives road accidents are getting more diverse.
Rare earth elements (REEs) are gaining attention due to rapid rise of modern industries and technological developments in their usage and residual fingerprinting. Cryptic entry of REEs in the natural ...resources and environment is significant; therefore, life on earth is prone to their nasty effects. Scientific sectors have expressed concerns over the entry of REEs into food chains, which ultimately influences their intake and metabolism in the living organisms.
Extensive scientific collections and intensive look in to the latest explorations agglomerated in this document aim to depict the distribution of REEs in soil, sediments, surface waters and groundwater possibly around the globe. Furthermore, it draws attention towards potential risks of intensive industrialization and modern agriculture to the exposure of REEs, and their effects on living organisms. It also draws links of REEs usage and their footprints in natural resources with the major food chains involving plants, animals and humans.
Scientific literature preferably spanning over the last five years was obtained online from the MEDLINE and other sources publishing the latest studies on REEs distribution, properties, usage, cycling and intrusion in the environment and food-chains. Distribution of REEs in agricultural soils, sediments, surface and ground water was drawn on the global map, together with transport pathways of REEs and their cycling in the natural resources.
Fourteen REEs (Ce, Dy, Er, Eu, Gd, Ho, La, Lu, Nd, Pr, Sm, Tb, Th and Yb) were plighted in this study. Wide range of their concentrations has been detected in agricultural soils (<15.9–249.1 μg g−1) and in groundwater (<3.1–146.2 μg L−1) at various sites worldwide. They have strong tendency to accumulate in the human body, and thus associated with kidney stones. The REEs could also perturb the animal physiology, especially affecting the reproductive development in both terrestrial and aquatic animals. In plants, REEs might affect the germination, root and shoot development and flowering at concentration ranging from 0.4 to 150 mg kg−1.
This review article precisely narrates the current status, sources, and potential effects of REEs on plants, animals, humans health. There are also a few examples where REEs have been used to benefit human health. However, still there is scarce information about threshold levels of REEs in the soil, aquatic, and terrestrial resources as well as living entities. Therefore, an aggressive effort is required for global action to generate more data on REEs. This implies we prescribe an urgent need for inter-disciplinary studies about REEs in order to identify their toxic effects on both ecosystems and organisms.
Soil contamination with toxic heavy metals (HMs) poses a serious threat to global food safety, soil ecosystem and human health. The rapid industrialization, urbanization and extensive application of ...agrochemicals on arable land have led to paddy soil pollution worldwide. Rice plants easily accumulate toxic HMs from contaminated agricultural soils, which ultimately accumulated in grains and enters the food chain. Although, physical and chemical remediation techniques have been used for the treatment of HMs-contaminated soils, however, they also have many drawbacks, such as toxicity, capital investment and environmental-associated hazards. Recently, engineered nanomaterials (ENMs) have gained substantial attention owing to their promising environmental remediation applications. Numerous studies have revealed the use of ENMs for reclamation of toxic HMs from contaminated environment. This review mainly focuses on HMs toxicity in paddy soils along with potential health risks to humans. It also provides a critical outlook on the recent advances and future perspectives of nanoremediation strategies. Additionally, we will also propose the interacting mechanism of HMs-ENMs to counteract metal-associated phytotoxicities in rice plants to achieve global food security and environmental safety.
Alternanthera yellow vein virus (AlYVV), a monopartite
, has been identified infecting a diverse range of crops and native plants in Pakistan, India, and China. However, distinctive yellow vein ...symptoms, characteristic of
infection, were observed on the
weed in Oman, prompting a thorough genomic characterization in this study. The results unveiled a complete genome sequence of 2745 base pairs and an associated betasatellite spanning 1345 base pairs. In addition, Sequence Demarcation Tool analyses indicated the highest nucleotide identity of 92.8% with a previously reported AlYVV-IN_abalpur_A_17:LC316182 strain, whereas the betasatellite exhibited a 99.8% nucleotide identity with isolates of tomato leaf curl betasatellite. Thus, our findings propose a novel AlYVV Oman virus (AlYVV-OM) variant, emphasizing the need for additional epidemiological surveillance to understand its prevalence and significance in Oman and the broader region. To effectively manage the spread of AlYVV-OM and minimize its potential harm to (agro)ecosystems, future research should focus on elucidating the genetic diversity of AlYVV-OM and its interactions with other begomoviruses.
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