AN IMPROVEMENT TOWARDS CONSIDERING PREFERENCES OF WEB SEARCH

With the rising number of web users using Smartphone in addition to its individualized service under examination, the environment of Smartphone does not make available user’s search rankings suitable to personal inclinations. Ontology-based user profiles can productively confine users’ content as well as location preferences and make use of the preferences to make relevant results for users. A realistic design was introduced for Personalized Mobile Search Engine by adopting the approach of meta-search which relies on the commercial search engines, to carry out a genuine search. In Personalized Mobile Search Engine, ontologies were accepted to structure the concept space intended for the reason that they not only can stand up for concepts but also hold the relations between concepts. The design of personalized mobile search engine addressed the issues such as restricted computational power on mobile devices, and minimization of data transmission. Proposed design accept server-client model in which user queries are forwarded towards a personalized mobile search engine server for processing training as well as re-ranking rapidly

Enhancing the Access Privacy of IDaaS System Using SAML Protocol in Fog Computing

Fog environment adoption rate is increasing day by day in the industry. Unauthorized accessing of data occurs due to the preservation of Identity and information of the users either at the endpoints or at the middleware. This paper proposes a methodology to protect and preserve the Identity during data transmission of the users. It uses fog computing for storage against security issues in the cloud and database environment. Cloud and database architectures failed to protect the data and Identity of users but the Fog computing based Identity management as a service (IDaaS) system can handle it with Security Assertion Mark-up Language (SAML) protocol and Pentatope based Elliptic Curve Crypto cipher. A detailed comparative study of the proposed and existing techniques is investigated by considering multi-authentication dialogue, security services, service providers, Identity, and access management

ECDSA-Based Water Bodies Prediction from Satellite Images with UNet

The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow to detect the water bodies. It uses a Nadam optimizer to reduce the losses. It also finds best-optimized parameters for the activation function, a number of nodes in each layer. This proposed model achieves integrity by embedding a security feature Elliptic Curve Digital Signature Algorithm (ECDSA). It generates a digital signature for the predicted area of water bodies which helps to secure the key and the detected water bodies while transmitting in a channel. Thus, the proposed model ensures the performance accuracy of 94% which can also work the same for edge detection, detection in blurred and low-resolution images. The model is highly robust

EXPERIMENTAL TOXICITY STUDIES OF SALMONELLA SEROVARS ISOLATED FROM PIGS, IN MICE AND GERMINATING SEEDS

ABSTRACT Experimental studies on the effects produced b

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS

Hash-based deep learning approach for remote sensing satellite imagery detection

Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof

TBSMR : A Trust-Based Secure Multipath Routing Protocol for Enhancing the QoS of the Mobile Ad Hoc Network

Mobile ad hoc network (MANET) is a miscellany of versatile nodes that communicate without any fixed physical framework. MANETs gained popularity due to various notable features like dynamic topology, rapid setup, multihop data transmission, and so on. These prominent features make MANETs suitable for many real-time applications like environmental monitoring, disaster management, and covert and combat operations. Moreover, MANETs can also be integrated with emerging technologies like cloud computing, IoT, and machine learning algorithms to achieve the vision of Industry 4.0. All MANET-based sensitive real-time applications require secure and reliable data transmission that must meet the required QoS. In MANET, achieving secure and energy-efficient data transmission is a challenging task. To accomplish such challenging objectives, it is necessary to design a secure routing protocol that enhances the MANET's QoS. In this paper, we proposed a trust-based multipath routing protocol called TBSMR to enhance the MANET's overall performance. The main strength of the proposed protocol is that it considers multiple factors like congestion control, packet loss reduction, malicious node detection, and secure data transmission to intensify the MANET's QoS. The performance of the proposed protocol is analyzed through the simulation in NS2. Our simulation results justify that the proposed routing protocol exhibits superior performance than the existing approaches.