Abstract-This paper investigates the effects of online exams on students' achievements and the students' perceptions of online and paper exams after taking an experimental online and paper based exams. Carefully designed exams that included various types of questions were attempted by male students in the faculty of computers and information technology and female students in the faculty of Education and Arts. The Moodle Quiz tool was used to design and conduct the online exams. The results of the online exams were compared with similar designed paper based exams. The students' performances in exams were measured in each question type (MCQ, TF, short, essay, numerical, and descriptive). Surprisingly, the mean and standard deviation statistical results were found to be similar between both paper based and online exams in the MCQ, TF, and numerical type of questions; while the essay questions results indicated that the students preferred to answer on paper rather than type on the computer screen. In the followed survey about their experiences with both exams, the students indicated to prefer certain aspects of online exams such as automatic results and feedback.
Distributing numerous services over the internet is called Cloud Computing. Applications and tools like networking, data storage, databases, servers, software are examples of the resources. The service provider is required to provide the resource always and from any location. However, the network is the most important factor in gaining access to data in the cloud. When leveraging the cloud network, the cloud threats take advantage. An intrusion Detection System (IDS) observes the network and detects and reports threats. The anomaly method is significant in Intrusion Detection Systems. IDS monitors known and unknown data whenever a virtual machine is developed. If any anonymous data is detected, the Intrusion Detection System identifies it using an anomaly classification algorithm and sends a report to the administrator. Naive Bayes, Decision tree (CART), Support Vector Machine, and random forest techniques are utilized in this work to classify unknown data. These algorithms are assisting in reducing the percentage of false alarms. This proposed work was carried out utilizing the WEKA tool for generating the report, yielding a best result in less computing time.
This paper uses wavelets in the detection comparison of breast cancer among the three main races in Malaysia: Chinese, Malays, and Indians followed by a system that evaluates the radiologist's findings over a period of time to gauge the radiologist's skills in confirming breast cancer cases. The db4 wavelet has been utilized to detect microcalcifications in mammogram-digitized images obtained from Malaysian women sample. The wavelet filter's detection evaluation was done by visual inspection by an expert radiologist to confirm the detection results of those pixels that corresponded to microcalcifications. Detection was counted if the wavelet-detected pixels corresponded to the radiologist's identified microcalcification pixels. After the radiologist's detection confirmation a new client-server radiologist recording and evaluation system is designed to evaluate the findings of the radiologist over some period of cancer detection working time. It is a system that records the findings of the Malaysian radiologist for the presence of breast cancer in Malaysian patients and provides a way of registering the progress of detecting breast cancer of the radiologist by tracking certain metric values such as the sensitivity, specificity, and receiver operator curve (ROC). The initial findings suggest that no single race mammograms are easier for wavelets' detections of microcalcifications and for the radiologist confirmation even though for this study the Chinese race samples detection average were a few percentages less than the other two races, namely the Malay and Indian races.
This paper presents a hybrid watermarking technique for medical images. The method uses a combination of three transforms: Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and singular value decomposition (SVD). Then, the paper discusses the results of applying the combined method on different medical images from eight patients. The images were watermarked with a small watermark image representing the patients' medical data. The visual quality of the watermarked images (before and after attacks) was analyzed using five quality metrics: PSNR, WSNR, PSNR-HVS-M, PSNR-HVS, and MSSIM. The first four metrics' average values of the watermarked medical images before attacks were approximately 32 db, 35 db, 42 db, and 40 db respectively; while the MSSM index indicated a similarity between the original and watermarked images of more than 97%. However, the metric values decreased significantly after attacking the images with various operations even though the watermark image could be retrieved after almost all attacks. In brief, the initial results indicate that watermarking medical images with patients' data does not significantly affect their visual quality and they can still be used by medical staff.
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