The purpose of this research is defining the main factors influencing on decision of management system on sensitive data in cloud. The framework is proposed to enhance management information systems decision on sensitive information in cloud environment. The structured interview with several security experts working on cloud computing security to investigate the main objective of framework and suitability of instrument, a pilot study conducts to test the instrument. The validity and reliability test results expose that study can be expanded and lead to final framework validation. This framework using multilevel related to Authorization, Authentication, Classification and identity anonymity, and save and verify, to enhance management

Purepolyaniline and doped with hydrochloric acid was prepared in different molarities at room temperature. The a.c electrical properties were stadied.AC conductivityσac (ω), is found to vary as ωS in the frequency range (100Hz-10MH), S< 1and decreases indicating a dominate hopping process. Thedielectric constant ε1and dielectric loss ε2 have been determined for bulk polyaniline. ε1 decrease with the increase frequency. Electrical conductivity measurements increase with the increases both of the amount of HCl and the dose of radiation. The dielectric investigations show decrease with dose radiation.

Wireless Sensor Networks (WSNs) are promoting the spread of the Internet for devices in all areas of
life, which makes it is a promising technology in the future. In the coming days, as attack technologies become
more improved, security will have an important role in WSN. Currently, quantum computers pose a significant
risk to current encryption technologies that work in tandem with intrusion detection systems because it is
difficult to implement quantum properties on sensors due to the resource limitations. In this paper, quantum
computing is used to develop a future-proof, robust, lightweight and resource-conscious approach to sensor
networks. Great emphasis is placed on the concepts of using the BB8

Simulation  of  the  Linguistic  Fuzzy Trust  Model  (LFTM)  over  oscillating  Wireless  Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Network

A high sensitivity, low power and low cost sensor has been developed for photoplethysmography (PPG) measurement. The PPG principle was applied to follow the dilatation and contraction of skin blood vessels during the cardiac cycle. A standard light emitting diodes (LEDs) has been used as a light emitter and detector, and in order to reduce the space, cost and power, the classical analogue-to-digital converters (ADCs) replaced by the pulse-based signal conversion techniques. A general purpose microcontroller has been used for the implementation of measurement protocol. The proposed approach leads to better spectral sensitivity, increased resolution, reduction in cost, dimensions and power consumption. The basic sensing configuration prese

The study of Lake Hamrin is of enormous importance in understanding changes in water levels and their impact on the local environment. It helps to clarify the role of climatic and human factors in the decline of its water resources from 2013 to 2023 in order to ensure sustainable solutions. Satellite imagery from Landsat 8 is used, and processing is performed using ArcGIS Pro and ENVI. The normalized vegetation index (NDVI) is applied to show the changes in the vegetation cover and reveal the amount of change in the surface water area of the lake. It is noted that the water area changes in one year as it increases in the rainy season and decreases in the summer, and it also varies from one year to another with the difference in the amount o

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip