EFUMS: Efficient File Upload and Mutli-Keyword Search over Encrypted Cloud Data

In the present era, cloud computing is built to provide many computation techniques and storage resources to the data user for later access. Data encryption is very important to ensure privacy before outsourcing it to the cloud server. Querying the cloud for encrypted data retrieval is a time-consuming process because of processing overhead and huge amount of data stored in cloud. In the existing system, the VPSearch scheme offers only verifiability of search results and privacy protection. It does not offer an efficient file uploading and index generation which consumes more time thereby slowing the searching process. It would be a challenging task to minimize the time to efficiently search on the cloud for a particular document. In order to overcome these challenges, we have proposed an efficient index generation scheme using tree based index technique with Greedy Depth-first search algorithm, that minimizes the

Efficacy of indigenous plant growth-promoting rhizobacteria and Trichoderma strains in eliciting resistance against bacterial wilt in a tomato

Bacterial wilt of tomato caused by Ralstonia solanacearum is a serious threat to tomato production worldwide. For eco-friendly management of bacterial wilt of tomato, the rhizospheric microorganisms belonging to the genera Bacillus (6 isolates), Brevibacillus (1 isolate), Pseudomonas (3 isolates), and Trichoderma (8 isolates) were studied for their ability to induce innate immunity in tomato, individually and in combination against R. solanacearum in greenhouse and field studies. In laboratory studies, maximum germination percent of 93%, vigor index of 1609 was noted in seed bacterization with P. fluorescens Pf3, followed by 91% germination, vigor index of 1593 in treatment with T. asperellum T8 over control. Under greenhouse conditions, protection against bacterial wilt in individual treatments with PGPRs ranged from 38 to 43% and Trichoderma sp. ranged from 39 to 43% in comparison to control. In comparison to individual seed treatment, among different combinations, maximum seed germination percent of 97% was recorded with combination P. fluorescens Pf3 + T. longibrachiatumUNS11. In greenhouse studies’ combination seed treatment with P. fluorescens Pf3 + T. longibrachiatumUNS11 offered an impressive 62% protection against bacterial wilt over control. Similarly, under field conditions, seed treatment with P. fluorescens Pf3 + T. longibrachiatumUNS11 resulted in 61% protection. The innate immunity triggered by eco-friendly seed treatment was analyzed by expression to defense-related enzymes such as peroxidase, phenylalanine ammonialyase, and polyphenol oxidase in comparison to control. This study indicated that the potential benefits of using combination treatments of beneficial microorganisms in effectively inducing resistance are possible for dual benefits of enhanced plant growth, tomato yield, and pathogen suppression

Cloud-based smart water quality monitoring system using IoT sensors and machine learning

Low water quality is a major concern in urban as well as rural areas. Consumption of contaminated water leads to several health hazards. Early water quality detection can prevent most of such health-related issues. Parameters such as conductivity, pH, nitrate, biochemical oxygen demand, fecal coliform are significant parameters in deciding the quality of water. These parameters which are collected from groundwater samples at different places are highly correlated to each other. Therefore, machine learning algorithms are used for classification. The data collected from sensors are further analyzed using a cloud-based environment Ubidots to support distributed computing. The cloud environment is connected to display units and mobile devices. To predict the quality of water it is necessary to check the values associated with the quality attributes and for that reason, a decision tree classification model is used. The dataset is broken into subsets that have decision nodes and leaf nodes to decide classifications. The IoT based sensors are deployed in the water tank to measure the quality parameters which are further sent to the cloud. The proposed framework predicts the water quality and assesses the performance of the decision tree classifier. Decision Tree is used to infer decision rules based on various parameters read through sensors

EKMPRFG: Ensemble of KNN, Multilayer Perceptron and Random Forest using Grading for Android Malware Classification

Android is the most popular Operating Systems with over 2.5 billion devices across the globe. The popularity of this OS has unfortunately made the devices and the services they enable, vulnerable to numerous security threats. As a result of this, a significant research is being done in the field of Android Malware Detection employing Machine Learning Algorithms. Our current work emphasizes on the possible use of Machine Learning techniques for the detection of malware on such android devices. The proposed EKMPRFG is applied for the classification of Android Malware after a preprocessing phase involving a hybrid Feature Selection model using proposed Standard Deviation of Standard Deviation of Ranks (SDSDR) and several other builtin Feature Selection algorithms such as Correlation based Feature Selection (CFS), Classifier SubsetEval, Consistency SubsetEval, and Filtered SubsetEval followed by Principal Component Analysis(PCA) for dimensionality reduction. The experimental results obtained on two data sets indicate that EKMPRFG outperforms the existing works in terms of Prediction Accuracy and Weighted F- Measure values

Framework for Cross Layer Energy Optimization in Wireless Sensor Networks

Cross-layer routing technique interacts among the various layers of the OSI model and exchanges information among them. It enhances the usage of network resources and achieves significant performance improvements in Quality of Service (QoS) parameters. The Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) routing algorithm consumes higher energy due to communication overhead and thus, a hierarchical model-based routing protocol named Cross-Layer Energy Efficient Scalable-Low Energy Adaptive Clustering Hierarchy Protocol (CLEES-LEACH) is proposed. This increases scalability using the Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol between the intermediary node and cluster head, with the overhead of latency. A Linear Programming model is used, which further makes use of scheduling to overcome latency. Energy efficiency and latency are addressed with the proposed cross-layer routing algorithm CLEESLEACH. The cross-layer design establishes Physical, Media Access Control (MAC), and Network layer interactions in the proposed algorithm. The present LEACH algorithm also increases the network overhead as there is no mechanism for communication among the network layer and consumes high energy. In the proposed algorithm CLEES-LEACH, latency is reduced to 25% and throughput is maximized to 20% compared to existing Energy-Efficient Distributed Schedule Based protocol (EEDS) and Integer Linear Programming (ILP) protocols. The energy consumption is also reduced to 20 % and the scalability is increased to 10 % compared to the existing LEACH and CL-LEACH

Cross-Diffusion and Viscoelastic Effects on Multidiffusive Porous Convection

The onset of triply cross-diffusive convection in a viscoelastic fluid-saturated porous layer is investigated as the study is found very relevant for describing natural phenomena (contaminant transport, underground water flow, improved oil recovery, polymer processing). A modified Darcy-Oldroyd-B model is used to describe the viscoelastic fluid flow in a porous medium with full cross-diffusion terms in the diffusivity matrix. A normal mode analysis yields an exact dispersion equation of fifth degree and accordingly the criterion for the onset of stationary and oscillatory convection is obtained. The numerical computations are carried out for diffusivity elements experimentally determined for lysozyme-sodium chloride-bovine serum albumin (BSA)-water system. Instability is found to occur via oscillatory mode for a certain choice of governing parameters. The relaxation and retardation viscoelastic parameters portray opposing contributions on the oscillatory onset and an increase in the relaxation parameter is to increase the range of retardation parameter up to which the oscillatory convection is preferred. The cross-diffusion is to either delay/hasten the onset of instability based on the magnitude of the stratifying agents. Even minute variations in the cross-diffusion elements indict complete change in the linear instability criteria. The topology of neutral curves disclosed the occurrence of disconnected closed convex oscillatory neutral curve revealing the requirement of three critical solute Darcy-Rayleigh numbers to state fully the instability criteria instead of the usual single value; a novel result ensured from the study. Moreover, the nature of instability for Oldroyd-B, Maxwell and Newtonian fluids turns out to be dissimilar for the same governing parameters

Synthesis of an Amino Phosphinodiselenoic Acid Ester and β-Amino Diselenides Employing P2Se5

The synthesis of a new class of amino phosphinodiselenoic acid ester and β-amino diselenides is conducted by employing a reaction between Nβ-protected aminoalkyl iodide and phosphorus pentaselenide (P2Se5) has been described. In the presence of protic solvents, amino phosphinodiselenoic acid esters were found to be the major products, whereas β-amino diselenides were formed exclusively when the reaction was carried out in polar aprotic solvents

Windowing Approach for Face Recognition Using the Spatial-Temporal Method and Artificial Neural Network

Face recognition (FR) is getting a lot of attention for a good reason in the field of research and making a big impact in areas such as computer vision and human-computer interaction. This paper proposes a FR model based on the windowing technique using discrete cosine transform (DCT), average covariance and artificial neural network (ANN). The windowing technique is used to divide the whole image into 4 × 4, 8 × 8 and 16 × 16 size of windows. The DCT is applied to each window to acquire DCT coefficients. The average covariance is calculated for the obtained DCT coefficient matrix. The calculation of an average covariance decreases the original size of the image by around 97%. The network is created, trained and tested to assess the performance of the network using nine standard face databases. Experimental results indicate that the proposed model achieves a higher recognition rate with a reduced number of features and computational intricacy compared with conventional methods

Evidence for the Enhanced Magnetic Properties of the Irradiated Ce3+ Substituted Co0.5Ni0.5 Ferrites

In the present study, synthesis and magnetic property characterization of Ce3+ substituted Co0.5Ni0.5Ce0.01Fe1.99O4 nanoferrites are reported. The synthesis of the sample is via modified solution combustion chemistry route. The influence of Ce3+ on structural and magnetic properties was studied as a function of 60Co gamma ray irradiation dose at the rates of 50kGy and 100kGy. Single phase formation of as synthesized sample was confirmed through X Ray Diffraction (XRD). Structural analysis was carried out by Rietveld refinement. Rietveld refined XRD data was well fitted with a structure of cubic spinel exhibiting space group of Fd3m. The lattice parameter of the studied samples increased due to the formation of Fe2+ ions under the ionizing effect of gamma radiation. Room temperature magnetic properties were explored by Vibration Sample Magnetometry (VSM). It reveals the enhanced saturation magnetization, remanence and magnetic pinning of the sample with Ce3+ substitution and gamma ray irradiation up to 50kGy. Further increase in dose rate at 100kGy has resulted in reduction of the magnetic properties. It is observed that the enhanced magnetic parameters are evidenced in the larger ionic radii Ce3+ substituted Co0.5Ni0.5Ce0.01Fe1.99O4 nanoferrites. These results are important for understanding the stability and performance of the ferrite based devices used near intense high energy radiation sources