Security and privacy aspects of cloud computing : a smart campus case study

The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost- effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future. © 2022, Tech Science Press. All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Ikram Haq” is provided in this record*

Ultrasound Evaluation of Liver in Patients Who Had History of Hepatitis C

Background: Hepatitis is an infection of liver. The disorder can be self-limiting or lead to fibrosis(scarring), cirrhosis, or liver cancer. Both acute infection and chronic sequelae, such as hepatitis C, cause significance morbidity and mortality in the hum of population. Hepatitis can be very high in general population. Our study evaluate the liver parenchyma in patients with history of Hepatitis C and provide descriptive to prevent future liver pathologies.The primary goal of our research is to delay or perhaps stop the progression of liver fibrosis, as well as to prevent liver disease.Grey scale ultrasonography has been reported to detect Ultrasonographic features of chronic disease resulting in decreased liver function and ultimately, liver failure and to help the patients with cured hepatitis C to check out the parenchymal damage and to give healthy lifestyle. Objective: To evaluate the ultrasonographic features of the liver in the patients who had history of hepatitis C. Study design: ur study design was retro prospective. Material and method: The retro prospective study was conducted in which data of 56 patients were taken. The data was collected from the radiology department of al-Razi health care and Jinnah hospital. After informed consent, data was collected through ultrasound machine. Out of 56 patients 27 were females and 29 were males. study duration was 4 months. Inclusion criteria includes patients with history of liver hepatitis C. Exclusion criteria was patients with other causes of liver disease, primary biliary cirrhosis, metabolic liver disease and liver transplant recipients except hepatitis C. Result: 56 patients were included in our study out of which 27 were females and 29 were males. The ultrasonographic findings of the patients having liver hepatitis C shows the cirrhosis in 44.6%, change in liver contour in 37.5%, nodularity in 42.9% and vascular changes in 58.9%. Conclusion: Some extent of hepatitis C diseases cannot be seen on CT or MRI but can be seen on grey scale ultrasonography due to its high resolution .so, it can be concluded that the grey scale ultrasonography is more efficient and authentic diagnostic equipment in assessing the liver hepatitis C as compared to the CT. Keywords: hepatitis C, cirrhosis, liver abnormalities, ultrasound. DOI: 10.7176/JHMN/91-08 Publication date:July 31st 202

Influence of various geometries on detection efficiency of polystyrene, polyvinyl-toluene, and sodium iodide detectors using Geant4

In this work, comparative study on energy dependence of absorbed, intrinsic, photo-peak and absolute total efficiency of polystyrene plastic scintillation fiber and polyvinyl-toluene detectors with NaI(Tl) scintillation detectors has been performed using Geant4 version 9.6 toolkit. The effects of geometry parameters on various efficiencies were investigated by varying detector radii, thickness and various source-to-detector configurations. These studies were carried out for both cylindrical and slab geometries for photon energy range of 10 keV-20 MeV using point isotropic sources and parallel beams of photons. Comparisons of the Geant4.9.6 based simulations for polystyrene scintillation fiber intrinsic efficiency as a function of photon energy and corresponding results obtained by earlier versions Geant4 (version 5.1) and Geant4 (version 8.1) show good agreements. The variation of the intrinsic efficiency with energy for polyvinyltoluene is also found to match very well with respective earlier results. This work confirms that the plastic scintillator based fibers and slab detectors are suitable for X-ray and low energy g-ray applications with energies typically below 50 keV with the optimum length of polystyrene scintillation fiber equal to 10 cm. For high energy range, cross talk remains an issue for polystyrene scintillation fiber and it is prominent in fibers having longer lengths and small diameters. Also, until the fiber radius is smaller than the incident photon beam, the fiber intrinsic efficiency increases with an increase in the radius

Segmentasi Pelanggan Berdasarkan Tingkat Loyalitas Menggunakan K-Means dan Seleksi Fitur LRFM pada Toko Online Retail

Customer experience is a key component in increasing sales numbers. Customers are important assets that must be kept up for a corporation or firm. Prioritizing customer service is one way to protect client loyalty. To ensure that service priority is right on target, this research was conducted on groups of consumers who are anticipated to have high business prospects. The 2011 retail online shop sales dataset with 379,980 records and eight char-acteristics was used. The length, recency, frequency, and monetary (LRFM) feature selection approach was used in the study process to select features for further segmentation using the K-Means data mining method to define consumer types. Following the completion of the research, clients were divided into four categories: Premium Loyalty, Inertia Loyalty, Latent Loyalty, and No Loyalty. The correct clustering results are displayed in the vali-dation test using the Silhouette Score Index technique, which yielded a score value of 0.943898. Based on the outcomes of this segmentation, business actors may prioritize providing clients with the proper service.Faktor besar dalam meningkatkan angka penjualan adalah pengalaman pelanggan. Pelanggan adalah aset yang berharga untuk bisnis atau perusahaan yang perlu dipertahankan. Pemberian prioritas layanan pelanggan dapat diterapkan sebagai strategi menjaga loyalitas pelanggan. Penelitian ini dilakukan untuk mengelompokan pelanggan yang diperkirakan memiliki prospek yang bagus bagi perusahaan sehingga pemberian prioritas layanan tidak salah sasaran. Data yang digunakan adalah 379980 data dari dataset penjualan toko online retail pada tahun 2011 yang berisi delapan atribut. Proses segmentasi dilakukan dengan metode data mining menggunakan K-Means dan metode feature selection LRFM. Penelitian yang dilakukan menghasilkan pengelompokan pelanggan menjadi empat kategori Premium Loyalty, Inertia Loyality, Laten Loyality dan Tanpa Loyalitas. Dari uji validasi yang dilakukan menggunakan metode Silhoutte Score Index menunjukan hasil clusterisasi yang tepat dengan nilai score 0,943898

The Effects of Water Friction Loss Calculation on the Thermal Field of the Canned Motor

The thermal behavior of a canned motor also depends on the losses and the cooling capability, and these losses cause an increase in the temperature of the stator winding. This paper focuses on the modeling and simulation of the thermal fields of the large canned induction motor by different calculation methods of water friction loss. The values of water friction losses are set as heat sources in the corresponding clearance of water at different positions along the duct and are calculated by the analytical method, loss separation test method, and by assuming the values that may be larger than the experimental results and at zero. Based on Finite volume method (FVM), 3D turbulent flow and heat transfer equations of the canned motor are solve numerically to obtain the temperature distributions of different parts of the motor. The analysis results of water friction loss are compared with the measurements, obtained from the total losses using the loss separation method. The results show that the magnitude of water friction loss within various parts of the motor does not affect the position of peak temperature and the tendency of the temperature distribution of windings. This paper is highly significant for the design of cooling structures of electrical machines

A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy

A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Abstract Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy