Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks

Telecommunication Systems - The Distance Vector-Hop (DV-Hop) algorithm is a well-known technique for wireless sensor networks node location due to its robustness, simplicity and cost effectiveness....     

Human Behavior Understanding in Big Multimedia Data Using CNN based Facial Expression Recognition

Mobile Networks and Applications - Human behavior analysis from big multimedia data has become a trending research area with applications to various domains such as surveillance, medical, sports,...     

An economic order quantity model of imperfect quality items with partial backlogging

This article deals with an economic order quantity (EOQ) model in which a certain percentage of a lotsize is of imperfect quality products. This percentage follows a uniform distribution function. During the inspection of the total lot-size, a stock-out situation may occur. In a stock-out situation, a partial fraction of the demand is adjusted by partial back ordering and the rest of the demand is considered as a case of lost sales. The associated integrated expected profit function is generalised for the general distribution function of imperfect quality products. Then the objective function is maximised. Also, three special cases of the general model are studied. A suitable numerical example is provided to illustrate the model and the solution procedure. Comparison between the general and special cases are also shown with the help of numerical examples. Sensitivity analysis of the optimal solutions with respect to all individual parameters of the general model is carried out.     

Maximising profits for an EPQ model with unreliable machine and rework of random defective items

This article deals with an economic production quantity (EPQ) model in an imperfect production system. The production system may undergo in ‘out-of-control’ state from ‘in-control’ state, after a certain time that follows a probability density function. The density function varies with reliability of the machinery system that may be controlled by new technologies, investing more costs. The defective items produced in ‘out-of-control’ state are reworked at a cost just after the regular production time. Occurrence of the ‘out-of-control’ state during or after regular production-run time is analysed and also graphically illustrated separately. Finally, an expected profit function regarding the inventory cost, unit production cost and selling price is maximised analytically. Sensitivity analysis of the model with respect to key parameters of the system is carried out. Two numerical examples are considered to test the model and one of them is illustrated graphically.     

Control Charts for Process Dispersion Parameter under Contaminated Normal Environments

Control charts are important statistical tool used to monitor fluctuations in the process location and dispersion parameters. The issues relating to the appropriate choice of control charts for the effective detection of process variability are addressed, and different control chart structures, such as Shewhart‐type, exponentially weighted moving average and cumulative sum are explored under ideal assumption of normality and contaminated normal environments, and hence, those control charts structures are identified which are more capable to detect aberrant changes in the process dispersion. Copyright © 2015 John Wiley & Sons, Ltd.     

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer withDeepLearning

Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.     

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.     

Optimizing Eg Orbital Occupancy of Transition Metal Sulfides by Building Internal Electric Fields to Adjust the Adsorption of Oxygenated Intermediates for Li-O2 Batteries

Li-O₂ batteries are acknowledged as one of the most promising energy systems due to their high energy density approaching that of gasoline, but the poor battery efficiency and unstable cycling performance still hinder their practical application. In this work, hierarchical NiS₂-MoS₂ heterostructured nanorods are designed and successfully synthesized, and it is found that heterostructure interfaces with internal electric fields between NiS₂ and MoS₂ optimized e_g orbital occupancy, effectively adjusting the adsorption of oxygenated intermediates to accelerate reaction kinetics of oxygen evolution reaction and oxygen reduction reaction. Structure characterizations coupled with density functional theory calculations reveal that highly electronegative Mo atoms on NiS₂-MoS₂ catalyst can capture more e_g electrons from Ni atoms, and induce lower e_g occupancy enabling moderate adsorption strength toward oxygenated intermediates. It is evident that hierarchical NiS₂-MoS₂ nanostructure with fancy built-in electric fields significantly boosted formation and decomposition of Li₂O₂ during cycling, which contributed to large specific capacities of 16528/16471 mAh g⁻¹ with 99.65% coulombic efficiency and excellent cycling stability of 450 cycles at 1000 mA g⁻¹. This innovative heterostructure construction provides a reliable strategy to rationally design transition metal sulfides by optimizing e_g orbital occupancy and modulating adsorption toward oxygenated intermediates for efficient rechargeable Li-O₂ batteries.     

Wireless Sensor Network Based Smart Grid Communications: Challenges,Protocol Optimizations,and Validation Platforms

Wireless Personal Communications - Smart grids, the next generation of electric grids, require the deployment of sophisticated monitoring and control systems to enhance their operational...