Developing an Efficient Secure Query Processing Algorithm on Encrypted Databases using Data Compression

Distributed computing includes putting aside the data utilizing outsider storage and being able to get to this information from a place at any time. Due to the advancement of distributed computing and databases, high critical data are put in databases. However, the information is saved in outsourced services like Database as a Service (DaaS), security issues are raised from both server and client-side. Also, query processing on the database by different clients through the time-consuming methods and shared resources environment may cause inefficient data processing and retrieval. Secure and efficient data regaining can be obtained with the help of an efficient data processing algorithm among different clients. This method proposes a well-organized through an Efficient Secure Query Processing Algorithm (ESQPA) for query processing efficiently by utilizing the concepts of data compression before sending the encrypted results from the server to clients. We have addressed security issues through securing the data at the server-side by an encrypted database using CryptDB. Encryption techniques have recently been proposed to present clients with confidentiality in terms of cloud storage. This method allows the queries to be processed using encrypted data without decryption. To analyze the performance of ESQPA, it is compared with the current query processing algorithm in CryptDB. Results have proven the efficiency of storage space is less and it saves up to 63% of its space.

The Implementation of a Red Object Tracking Algorithm with a Single, Static Camera

In recent years, activity recognition and object tracking are receiving extensive attention due to the increasing demand for adaptable surveillance systems. Activity recognition is guided by the parameters such as the shape, size, and color of the object. This article purposes an examination of the performance of existing color-based object detection and tracking algorithms using thermal/visual camera-based video steaming in MATLAB. A framework is developed to detect and track red moving objects in real time. Detection is carried out based on the location information acquired from an adaptive image processing algorithm. Coordinate extraction is followed by tracking and locking the object with the help of a laser barrel. The movement of the laser barrel is controlled with the help of an 8051 microcontroller. Location information is communicated from the image-processing algorithm to the microcontroller serially. During implementation, a single static camera is used that provides 30 frames per second. For each frame, 88 ms are required to complete all three steps from detection to tracking, to locking, so a processing speed of 12 frames per second is implemented. This repetition makes the setup adaptive to the environment despite the presence of a single static camera. This setup can handle multiple objects with shades of red and has demonstrated equally good results in varying outdoor conditions. Currently, the setup can lock only single targets, but the capacity of the system can be increased with the installation of multiple cameras and laser barrels.

Classification of Some Fruits using Image Processing and Machine Learning

In this study, an image processing algorithm and classification unit were developed to classify the fruits according to their size and color characteristics. For this purpose, a total of 300 fruits (50 fruit samples from each of the Starkrimson Delicious and Golden Delicious apple varieties, Washington Navel and Valencia Midknight orange varieties, Ekmek and Eşme quince varieties) were used in the experiments. The size and color values measured with a caliper and a spectrophotometer were entered in the developed image processing algorithm to determine the success rates of classifying the fruits. The integration of image processing algorithm with the classification unit classified 88%, 100%, 96%, 82%, 86%, respectively. On the other hand, the size and color values read in fruits with the image processing algorithm were evaluated using predictive techniques used in data mining. For this purpose, K Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes classification and Multilayer Perceptron Neural Network (MLP) algorithms were used. Algorithms were run with 10-fold cross validation method. In the training of artificial classifiers, the success was 93.6% for KNN, 90.3% for DT, 88.3% for Naive Bayes, 92.6% for MLP and 94.3% for RF.

Development of a Natural Language Processing Algorithm for the Classification of Suspicious Liver Lesions from Radiology Reports

Here, we developed and validated a highly generalizable natural language processing algorithm based on deep-learning. Our algorithm was trained and tested on a highly diverse dataset from over 2,000 hospital sites and 500 radiologists. The resulting algorithm achieved an AUROC of 0.96 for the presence or absence of liver lesions while achieving a specificity of 0.99 and a sensitivity of 0.6.

A Four Stage Image Processing Algorithm for Detecting and Counting of Bagworm, Metisa plana Walker (Lepidoptera: Psychidae)

The bagworm is a vicious leaf eating insect pest that threatens the oil palm plantations in Malaysia. The economic impact from defoliation of approximately 10% to 13% due to bagworm attack might cause about 33% to 40% yield loss over 2 years. Due to this, monitoring and detecting of bagworm populations in oil palm plantations is required as the preliminary steps to ensure proper planning of control actions in these areas. Hence, the development of an image processing algorithm for detection and counting of Metisa plana Walker, a species of Malaysia’s local bagworm, using image segmentation has been researched and completed. The color and shape features from the segmented images for real time object detection showed an average detection accuracy of 40% and 34%, at 30 cm and 50 cm camera distance, respectively. After some improvements on training dataset and marking detected bagworm with bounding box, a deep learning algorithm with Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm was applied leading to the percentage of the detection accuracy increased up to 100% at a camera distance of 30 cm in close conditions. The proposed solution is also designed to distinguish between the living and dead larvae of the bagworms using motion detection which resulted in approximately 73–100% accuracy at a camera distance of 30 cm in the close conditions. Through false color analysis, distinct differences in the pixel count based on the slope was observed for dead and live pupae at 630 nm and 940 nm, with the slopes recorded at 0.38 and 0.28, respectively. The higher pixel count and slope correlated with the dead pupae while the lower pixel count and slope, represented the living pupae.

Developing A Deep Learning Natural Language Processing Algorithm For Automated Reporting Of Adverse Drug Reactions

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 150,000 unlabelled discharge summaries; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.934 (95% CI 0.931 - 0.955) for the task of identifying discharge summaries containing ADR mentions.