University of Wah Journal of Computer Science

QoS-Aware Efficient Tasks Scheduling in Heterogeneous Cloud Computing Environment

2023-02-27T05:45:19+00:00

Quality of Service (QoS) is a set of technologies and techniques that are used to manage and prioritize network traffic to ensure that important data is transmitted efficiently and effectively. It helps to ensure that critical applications and services receive the necessary bandwidth and resources to function properly, while less important traffic is given a lower priority. This is accomplished by classifying and marking packets and then further process on it. Task scheduling is the process of arranging, coordinating, and managing the execution of tasks, typically in a computer system or network. This can include the allocation of resources such as CPU time, memory, and storage, as well as the order in which tasks are executed. Task scheduling algorithms are used to determine the most efficient way to execute a set of tasks, based on factors such as priority, resource requirements, and dependencies between tasks. A heterogeneous cloud environment refers to a scenario where multiple cloud computing platforms from different vendors are used in combination to deliver a specific set of services. This can include using a mix of public, private, and hybrid clouds, as well as different types of infrastructure such as IaaS, PaaS, and SaaS. The goal of a heterogeneous cloud environment is typically to leverage the strengths of each platform in order to optimize cost, performance, and scalability. In cloud computing, the task scheduling problem necessitates the effective mapping of workloads to virtual resources. The genetic-based algorithm BGA will be introduced in a study in order to increase the makespan and its resource consumption. On the other hand, the SGA is concerned with convergence speed. The strategies given are compared to several current meta-heuristic and heuristic techniques.

Secure Medical Imaging Data Using Cryptography with Classification

2023-05-20T14:54:33+00:00

Medical imaging data in today's healthcare information systems is an essential part of diagnostics. The secure medical imaging data plays a critical role in current time but today it is complex task of maintaining data privacy so the main objective of this study to solve this problem. In this project firstly we secure the MRI images of the brain using cryptography. In this process input images are encrypted & decrypted using public key cryptography and supplied as an input to the pre-trained convolutional neural network such as Alex-net. The model comprises of the 25 layers such as convolutional, batch-normalization, ReLU and max-pooling etc. The classification between the tumor and healthy images has been performed using SoftMax layer. The performance of the proposed model has been tested on publically available BRATS-2020 Challenging dataset. The proposed model achieved up to the 97% prediction scores that are far better as compared to the latest published research work in this domain.

A Novel CNN-RNN Model for E-Cheating Detection Based on Video Surveillance

2023-05-21T04:03:54+00:00

Nowadays, everything needs to be digitized, and scientific knowledge is constantly bringing comfort and change to everyday life. Autonomous systems have become a prominent technology in recent years, with a variety of applications in different fields. Though our proposed system is designed to maintain academic integrity in exams. The system uses computer vision techniques to monitor the behavior of students during the exam and detect any suspicious activities, such as looking at someone else's paper or using unauthorized materials. The proposed E-Cheating Detection consists of four core steps: 1) Student/Person Detection and Tracking, 2) Detect suspicious activities, 3) Generating alerts, and 4) Mark attendance. Student detection from videos is performed by using YOLOv7 and DeepSort tracker is used to track the detected persons that are being detected by the YOLOv7 algorithm. To classify suspicious activities (such as the exchange of paper, and giving codes to one another, etc.) the system uses CNN-RNN architecture in which the inceptionV3 model is used for feature extraction. The system will generate real-time alerts for suspicious behavior by sending an email via SMTP to the exam administration /invigilator. The system marks the student's attendance by recognizing and matching student faces that are stored in the database. The performance of the system will be evaluated by conducting a series of experiments using simulated scenarios, and the results will demonstrate the effectiveness of the proposed system in detecting suspicious activities during physical exams. The proposed system has the potential to promote exam integrity and create a fair environment for all students, ultimately improving the education system's quality.

Plant Disease Detection Using Deep Learning: A Survey

2023-05-20T04:56:13+00:00

The research on maize diseases is described in this study of current literature. The most valuable findings are extracted from the previous work of researchers and presented in a compiled form. The study demonstrates how the model and approach may be designed from a new viewpoint, which can then lead to improved outcomes. Following this, datasets that are accessible to the public are described and identified, and then the proposed procedures and the findings are tested and verified using these datasets. Performance metrics along with their respective formulae are discussed to demonstrate how these measures might be used to evaluate the effectiveness of research activity.

Advancements In Artificial Intelligence for Brain Tumor Detection: A Comprehensive Survey of Methods, Techniques, and Deep Learning Models

2023-04-25T07:57:02+00:00

Brain tumors are a group of diseases characterized by abnormal growths of cells in the brain that can cause severe neurological symptoms. In recent years, the advent of artificial intelligence (AI) techniques has shown great promise in enhancing brain tumor detection. This survey research discusses methods and techniques used for AI-based brain tumor detection. Brain tumors pose significant health risks, necessitating accurate and timely detection for effective treatment. The study defines brain tumors and emphasizes the need for precise detection methods due to tumor variations. Biomarkers associated with brain tumors are investigated, highlighting their potential as diagnostic and prognostic indicators. The utilization of deep learning (DL) models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and 3D CNNs, is examined, providing a comparative analysis of their strengths and limitations. The importance of datasets, such as TCIA, BRATS, and ISLES, in training and evaluating AI models for brain tumor detection is discussed. This survey aims to contribute to the understanding and progress of AI-based brain tumor detection along with the comparison of some deep learning models, providing insights for researchers and healthcare professionals working towards improving patient outcomes.