Modelling of high-dimension chaotic, noisy, and non-stationary time series of complex fractal dynamics is a big challenge. In this research work , a novel approach of Leverage Convolution LC ARFIMA– GARCH model is presented for sequential learning of irregular, fractal dynamic patterns of stochastic time series dynamics. Different classical methods including the auto-regressive approach indecently are unable to capture fragile patterns and consequently convert some weak signals into random errors by creating white noise patterns. The Convolution Leverage provides an additional degree of freedom for critical noisy points and asymmetrical distribution dynamics to regulate the frequency of imbalance and skewed observation. The designed transformation in the ARFIMA model preserves the loss of information by shifting the skewed data distribution toward a normal pattern. The pattern of population parameters in the proposed leverage paradigm provides out of box approach to track and extract additional information on population variance patterns in the form of an additional degree of freedom to stabilize imbalance signals. The model can provide reliable performance for long-range dependence, particularly for mean reversion chaotic phase variation at critical non-differentiable points. The performance of the dynamic model is verified on chaotic real data of the Ireland Stock Market (ISEQ). The result statistics confirmed the optimized outcome with the addition of the GARCH heteroscedastic multimodal and radial basis neural network . The novel technique can help to address inherited challenges in the imbalance learning and extreme events of the time series modeling by monitoring the chaotic trajectory of fractal physical phenomena , particularly in, finance healthcare, climate, and intelligent sustainability .

In this investigative study, the electro‐magneto hydrodynamic (EMHD) influence on a nano viscous fluid model is scrutinized by designing an artificial neural network (ANN) paradigm using a neuro‐heuristic approach (NHA) through the combination of GAs (genetic algorithms) and one of the most efficient locally searching solver SQP (sequential quadratic programming), i.e., NHA‐GA‐SQP. The fluid flow for the proposed problem is initially interpreted in the form of PDEs and then utilization of suitable similarity transformation on these PDEs yields in terms of a stiff nonlinear system of ODEs. The numerical results of the suggested fluidic model based on the variation of its physically existing parameters are calculated through the NHA‐GA‐SQP solver to detect the variation in velocity, thermal gradient, and concentration during the fluid flow. A detailed analysis of obtained outcomes through the NHA‐GA‐SQP algorithm and their comparison with the reference results estimated via the Adams method are presented. The calculation of the proposed solver’s accuracy, stability, and consistency through various statistical operators is also involved in the current inspection.

Electricity theft is the primary cause of electrical power loss that significantly affects the revenue loss and the quality of electrical power. Nevertheless, the existing methods for the detection of this criminal behavior of theft are diversified and complicated since the imbalanced nature of the dataset, and high dimensionality of time-series data make it challenging to extract meaningful information. This paper addresses these problems by developing a novel electricity theft detection model, integrating three algorithms in a pipeline. The proposed method first applies the synthetic minority oversampling technique (SMOTE) for balancing the dataset, secondly integration of kernel function and principal component analysis (KPCA) for the feature extraction from high dimensional time-series data, and support vector machine (SVM) for the classification. Besides, the performance of the proposed pipeline is measured using a comprehensive list of performance metrics. Extensive experiments are performed by using real electricity consumption data, and results show that the proposed method outperforms other methods in terms of theft detection.

A large amount of data is involved in an effective and timely exchange of traffic information between vehicles in Vehicular Ad-hoc Networks (VANETs), which ensures efficiency and reliability. VANETs assist in sharing traffic information effectively and timely to improve traffic efficiency and reliability. However, less storage capability and selfish behavior of the vehicles are important issues that need to be tackled. Moreover, traditional storage mechanisms require the involvement of third parties, which are insecure, untrustworthy, non-transparent, and unreliable. To overcome these issues, we proposed a blockchain-based data storage scheme for VANETs by exploiting the benefits of the Interplanetary File System (IPFS), which is deployed on Road Side Units (RSUs). Furthermore, RSUs are able to receive the aggregation packet comprising of the event information acquired from the vehicles. After receiving and verifying the aggregation packet, the RSU stores the event's information in IPFS and the reputation values of vehicles in blockchain. Moreover, we proposed an incentive mechanism in this work, in which monetary incentives are given to the repliers who agree with the vehicle regarding the event information. The incentives are given by the initiator after verifying the signatures of the repliers. All the transactions involved in the incentive process are stored in blockchain. The simulation results prove the efficiency of the proposed scheme in terms of transaction cost and storage savings in VANETs.

The significance of electricity cannot be overlooked in terms of advancements in economic and technological fields. In this study, Ensemble Empirical Mode Decomposition (EEMD) method in combination with the Ensemble Bi-Long Short Term Memory (EBiLSTM) and Support Vector Machine (SVM) is used. Non linear and non stationary IMFs are forecast using EBiLSTM forecasting algorithm as it performs efficiently in complex and non linear scenario. Whereas, linear IMFs are forecast using SVM as EBiLSTM take high computational time unlike SVM. The proposed technique EEMD-EBiLSTM-SVM gives good results.

In this paper, a Hybrid Deep Neural Network (HDNN) is proposed in this work, which is the combination of Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Particle Swarm Optimization (PSO), termed as CNN-GRU-PSO HDNN. In this paper, real time electricity consumption data of consumers is used, which is taken from an easily available online source, named as State Grid Corporation of China (SGCC). The original dataset consists of actual values along with the erroneous and missing values. The pre-processing steps are performed initially to refine the data. After that, feature selection and extraction are performed using CNN, which reduce both the dimensionality and the redundancy present in the dataset. Furthermore, the classification of provided data into honest and fake consumers is done using GRU-PSO technique. The proposed HDNN model's performance is then compared with various benchmark techniques like Logistic Regression (LR), Support Vector Machine (SVM), Long Short Term Memory (LSTM) and GRU. The efficiency of the proposed model is validated using various performance parameters like Area Under the Curve (AUC), precision, accuracy, recall and F1-Score. The simulation results show that the proposed model outperforms the existing techniques in terms of ETD and class imbalanced issues. Moreover, the proposed model is also more robust and accurate than the existing methods.

The development of Industry 4.0 has provided the possibility to meet frequent changes in product type and batches, a sharp decline in the delivery cycle, constraints of quality cost, and other relevant parameters of customized production mode. Intelligent manufacturing, as a core of Industry 4.0, represents a deep integration of new IT technologies, such as the industrial Internet of Things and service-oriented architecture, and manufacturing process. To realize intelligent manufacturing, this article introduces a cloud-assisted and edge-decision-making manufacturing architecture that contains a cloud and production edges. An intelligent production edge is designed to provide the traditional devices the abilities of data access and self-decision making. Besides, the proposed architecture is modeled as a multiagent system with the edge intelligence support, describing the agent-based reconfiguration mechanism from the three aspects, namely, agent interaction, agent behavior, and negotiation mechanism. The experimental results show that the reconfigurable method based on the proposed architecture can be used in the mixed-flow production scenario based on random orders, to improve the adaptability and robustness.

This paper explored cooperative relaying in the presence of energy constrained relays with data storage facility. The relays depend only on the source signal to harvest energy and forward signal to the destination. The relay is selected according to the instantaneous strength of the wireless link. The strongest link among all links on both sides i.e., source-relay and relaydestination links is selected for relay to receive or transmit data, respectively. Two protocols are used for energy harvesting and information transfer namely: ”power splitting based relaying” and ”time switching based relaying”. We evaluate the outage probability performance of the presented scheme using Monte Carlo simulations. The results show that TSR performs better than PSR protocol.

This piece of work presents a novel design for buffer-aided relaying to increase the diversity gain. In this design, we used random buffer access method and associate each buffer location with its own antenna resource. Thus, each buffer location acts as an independent entity known as the virtual relay. The relay selection is based on the instantaneous strength of wireless link and the status of buffers. Markov chain is used to illustrate the growth of buffer status and to derive the closed-form expressions for the outage probability and diversity gain. The proposed design achieves the diversity gain of KL as compared to current buffer-aided max-link and max-max schemes having the diversity gain of 2K and K, respectively. Moreover, the proposed design achieves less delay at low SNR and 1+KL at high SNR. Analytical results are validated via simulation results.

Underwater Wireless Sensor Networks (UWSNs) are getting growing interest because of wide-range of applications. Most applications of these networks demand reliable data delivery over longer period in an efficient and timely manner. However, resource-constrained nature of these networks makes routing in a harsh and unpredictable underwater environment challenging. Most existing schemes either employ static or mobile sink for data collection. However, in former sensors near the sink deplete out their energy more quickly which limits network lifetime. Mobile sink based schemes are not suitable for delay-sensitive large-scale applications. Unlike prior work, this paper presents a novel Advanced AUV-aided Energy Efficient Routing Protocol (AAEERP) for reliable data delivery. To prolong network lifetime, AAEERP employs an autonomous underwater vehicle to collect data from gateways. To minimize energy consumption, we use a shortest path tree algorithm while associating sensor nodes with the gateways and devise a criterion to limit the association count of nodes. Moreover, the role of gateways is rotated to balance the energy consumption. To prevent data loss, AAEERP allows dynamic data collection time to AUV depending up the count of member sensors for each gateway. The performance of the AAEERP is validated through simulations. Simulation results demonstrate the effectiveness of AAEERP in terms of various performance metrics.