Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things(IoMT)enabled e-healthcare has the potential to greately improve conventional healthcare services significantly.However,security and privacy become major issues of IoMT because of the restricted processing abilities,storage,and energy constraints of the sensors.Therefore,it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors.In order to ensure security on sensitive medical data,effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers.In this view,this study designs an effective metaheuristic optimization based encryption with user authentication(EMOE-UA)technique for IoMT environment.This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity.Moreover,the EMOE-UA technique employs optimal multikey homomorphic encryption(OMKHE)technique to encrypt the IoMT data.Furthermore,the improved social spider optimization algorithm(ISSOA)was employed for the optimal multikey generation of the MKHE technique.The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects.The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and composite.So,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated way.Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery.The advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals.This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)Model.The proposed model operates on three major processes namely signal representation,feature extraction,and classification.The proposed model uses a Continuous Wavelet Transform(CWT)is for preprocessed representation of the original vibration signal.In addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level features.Besides,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer.Finally,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults proficiently.Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset.The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing dataset.The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

Web Application Commercial Design for Financial Entities Based on Business Intelligence

Multiple customer data management has become a focus of attention in big organizations.Although much information is available,it does not translate into significant profitable value-added services.We present a design of a commercial web application based on business intelligence that generates information on social and financial behavior of clients in an organization;with the purpose of obtain additional information that allows to get more profits.This app will provide a broader perspective for making strategic decisions to increase profits and reduce internal investment costs.A case in point is the financial sector,a group of financial entities were used to make measurements and test them.A design to build a web application aimed at achieving a large and ambitious goal by means of defined tools reflecting clients’business needs is proposed.In this research,different techniques and technologies are explored,such as diagrams,frameworks,design,architecture,model entity-relationship,tables,equations,mental maps and development tools.Through the Personal Software Process methodology and with the help of information extraction,consolidation,and visualization,the implementation can be carried out.This article provides the importance of implementing business intelligence in an organization and expands on the steps needed for the implementation of this valuable technology.

Effect of low level laser therapy on dental pulp during orthodontic movement

AIM: To validate the protocol described here to be used in future clinical trials related to the effect of laser therapy on dental pulp. METHODS: Histologically treated samples from eight human healthy premolar teeth obtained from the middle root level were distributed in four groups: group 1(G1) absolute control; group 2(G2) only laser irradiation; group 3(G3) exposed only to orthodontics; and group 4(G4) treated with orthodontics and laser. Laser treatment was performed at 830 nm wavelength, 100 mW(energy 80 J/cm2, 2.2 J), for 22 s in the vestibular surface and 22 s in the palatal surface, 1 mm away from the dental root mucosa. Three staining methods were performed: hematoxylin-eosin(HE), Masson's Trichrome method and Gomori's method.RESULTS: The pulp histology parameters were evaluated and the results classified in to 3 parts: an inflammatory response, soft tissue response(dental pulp) and hard tissue response(dentin and predentin). There was no inflammation(chronic or acute) in any of the evaluated groups. The zones of pulp necrosis were found in one premolar of G3 and in one of G4; in groups G2 and G4 there was higher angiogenesis than in the other two groups. G4 group presented the highest level of vascularization. A reduced nerve density was observed in G3. A G2 specimen showed increased nerve density. A higher rate of calcification was observed in G1 compared to G2. Denticles, either real or false, were observed in G1, G2 and G3. Sclerosis of dentin and focal dentin loss was observed among all the groups. Secondary dentin was present in one sample in G1 and G2. A necrosis zone was found in one sample of G3 and G4. No differences between groups were observed in the odontoblast irregularity layer but the layer was wider in the group treated with laser only. A notable difference was detected in reduction of the cell-free layer between the groups G1 and G4. The findings in pulp tissue favor its adaptative response against dental movement induced by orthodontics. No definitive conclusions may be derived as this is a pilot study. CONCLUSION: The protocol described here was shown to be an effective method to evaluate changes in dental pulp submitted to low level laser in teeth under orthodontic movement.

Procedural modeling applied to the 3D city model of Bogota:A case study

Background Computer Generated Animations(CGA),when applied to three-dimensional(3D)city models(3DCM),can be used as powerful tools to support urban decision-making.This leads to a new paradigm,based on procedural modeling,that allows the integration of known urban structures.Methods This paper introduces a new workflow for the development of high-quality approximations of urban models in a short time and enables facilities to be imported from other cities into a given city model,following specific generation rules.Results Thus,this workflow provides a very simple approach to observe,study,and simulate the implementation of models already developed in other cities,in a city where they are not yet adopted.Examples of these models include all types of mobility systems and urban infrastructure.Conclusions This allows us to perceive the environmental impact of certain decisions in the real world,as well as to carry out simple simulations to determine the changes that can occur in the flows of people,traffic,and other city activities.

Development of a Module for Measuring Electrical Variables in Power Transformers Based in IoT,to Manage and Monitoring by Telemetry Mechanism

This work shows the development of a module that performs measurements of electrical variables in a low voltage power transformer.These variables are sent by means of the IEEE802.11 standard,connecting to a database stored in the cloud;associating with the meter IoT concepts,this to allow a client to perform an analysis,monitoring and management of their electrical network.For the construction of this module,non-invasive current sensors connected to a three-phase meter are used and a communication card is used that allows data to be extracted from the meter and sent to the cloud database.This module,to convert a conventional electrical power transformer into a smart electrical power transformer,making it a Smart Object and thus extract information from it telemetrically in real time.

Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.

Human papillomavirus in upper digestive tract tumors from three countries

AIM: To clarify human papillomavirus (HPV) involvement in carcinogenesis of the upper digestive tract of virological and pathological analyses. METHODS: The present study examined the presence of HPV in squamous cell carcinomas of the oral cavity (n = 71), and esophagus (n = 166) collected from Japan, Pakistan and Colombia, with different HPV exposure risk and genetic backgrounds. The viral load and physical status of HPV16 and HPV16-E6 variants were examined. Comparison of p53 and p16INK4a expression in HPV-positive and HPV-negative cases was also made. RESULTS: HPV16 was found in 39 (55%) oral carcinomas (OCs) and 24 (14%) esophageal carcinomas (ECs). This site-specific difference in HPV detection between OCs and ECs was statistically significant (P < 0.001). There was a significant difference in the geographical distribution of HPV16-E6 variants. Multiple infections of different HPV types were found in 13 ECs, but multiple infections were not found in OCs. This difference was statistically significant (P = 0.001). The geometric means (95% confidence interval) of HPV16 viral load in OCs and ECs were 0.06 (0.02-0.18) and 0.12 (0.05-0.27) copies per cell, respectively. The expression of p16INK4a proteins was increased by the presence of HPV in ECs (53% and 33% in HPV-positive and-negative ECs, respectively; P = 0.036), and the high-risk type of the HPV genome was not detected in surrounding normal esophageal mucosa of HPV-positive ECs. CONCLUSION: Based on our results, we cannot deny the possibility of HPV16 involvement in the carcinogenesis of the esophagus.

Interactive Middleware Services for Heterogeneous Systems

Computing has become more invisible, widespread and ubiquitoussince the inception of the Internet of Things (IoT) and Web of Things. Multipledevices that surround us meet user’s requirements everywhere. Multiple Middleware Framework (MF) designs have come into existence because of the rapiddevelopment of interactive services in Heterogeneous Systems. This resulted inthe delivery of interactive services throughout Heterogeneous Environments(HE). Users are given free navigation between devices in a widespread environment and continuously interact with each other from any chosen device. Numerous interactive devices with recent interactive platforms (for example, SmartPhones, Mobile Phones, Personal Computer (PC) and Personal Digital Assistant(PDA)) are available in the market. For easy access to information and servicesirrespective of the device used for working and even at the drastic change ofthe environment, the execution of applications on a broad spectrum of computingdevices is propelled by the availability of the above-mentioned platforms. Different applications that need interoperability to coordinate and correspond with eachother should be facilitated. Using a standard interface and data format, HE mustlink various devices from various platforms together to communicate with eachother. To aid the interactive services performed by a middleware framework thatoperates on Application Programming Interface (API) over HEs, this issue aims toendorse an Adaptable Service Application Programming Interface (ASAPI).

QoS in FANET Business and Swarm Data

This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks(FANET).Each drone has the ability to send and receive information(like a router);and can behave as a hierarchical node whit the intregration of three protocols:Multiprotocol Label Switch(MPLS),Fast Hierarchical AD Hoc Mobile(FHAM)and Internet Protocol version 6(IPv6),in conclusion MPLS+FHAM+IPv6.The metrics analyzed in the FANET are:delay,jitter,throughput,lost and sent packets/received.Testing process was carried out with swarms composed of 10,20,30 and 40 units;In this work,the stage with 40 droneswas analyzed showing registration processes,and sentmessages sequences between different drones that were part of the same swarm.A special analysis about the traffic between drones(end-to-end)was carried out,as well as the possible security flaws in each drone and the current status and future trends in real services.Regarding future trends,in a real environment,we took as a starting point,metrics results obtained in the simulation(positive according to the obtained results).These results gave us a clear vision of how the network will behave in a real environment with the aim to carry out the experiment on a physical level in the near future.This work also shows the experience quality from the service quality metrics obtained through a mathematical model.This quality of experience model will allow us to use it objectively in the agricultural sector,which is a great interest area and is where we are working with drones.Finally in this article we show our advances for a business model applied to the aforementioned agricultural sector,as well as the data analysis and services available to the end customer.These services available to the end customer have been classified into a basic,medium,advanced and plus level.

Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the Liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method.

Quench Dynamics of Neutral Atoms in Out-Equilibrium One-Dimensional Optical Lattices

A quantum simulator is proposed for nucleation and growth dynamics using an out-of equilibrium optical lattice. We calculate the density of neutral atoms in the lattice and we establish the connection with the KolmogorovMehl-Johnson-Avrami model. Here we show that an Avrami equation can describe most of the evolution in time of the population growth in the lattice, coherence between neutral atoms leads a complex growth rate.