Towards effective and efficient online exam systems using deep learning-based cheating detection approach

With the high growth of digitization and globalization, online exam systems continue to gain popularity and stretch, especially in the case of spreading infections like a pandemic. Cheating detection in online exam systems is a significant and necessary task to maintain the integrity of the exam and give unbiased, fair results. Currently, online exam systems use vision-based traditional machine learning (ML) methods and provide examiners with tools to detect cheating throughout the exam. However, conventional ML methods depend on handcrafted features and cannot learn the hierarchical representations of objects from data itself, affecting the efficiency and effectiveness of such systems. The proposed research aims to develop an effective and efficient approach for online exam systems that uses deep learning models for real-time cheating detection from recorded video frames and speech. The developed approach includes three essential modules, which constantly estimate the critical behavior of the candidate student. These modules are the front camera-based cheating detection module, the back camera-based cheating detection module, and the speech-based detection module. It can classify and detect whether the candidate is cheating during the exam by automatically extracting useful features from visual images and speech through deep convolutional neural networks (CNNs) and the Gaussian-based discrete Fourier transform (DFT) statistical method. We evaluate our system using a public dataset containing recorded audio and video data samples collected from different subjects carrying out several types of cheating in online exams. These collected data samples are used to obtain the experimental results and demonstrate the proposed work\u27s efficiency and effectiveness

A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection

The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. Many techniques have recently been proposed; however, these techniques face significant challenges due to the continuous emergence of new threats that are not recognized by the existing detection systems. In this paper, we propose a novel two-stage deep learning model based on a stacked auto-encoder with a soft-max classifier for efficient network intrusion detection. The model comprises two decision stages: an initial stage responsible for classifying network traffic as normal or abnormal using a probability score value. This is then used in the final decision stage as an additional feature for detecting the normal state and other classes of attacks. The proposed model is able to learn useful feature representations from large amounts of unlabeled data and classifies them automatically and efficiently. To evaluate and test the effectiveness of the proposed model, several experiments are conducted on two public datasets: an older benchmark dataset, the KDD99, and a newer one, the UNSW-NB15. The comparative experimental results demonstrate that our proposed model significantly outperforms the existing models and methods and achieves high recognition rates, up to 99.996% and 89.134%, for the KDD99 and UNSW-NB15 datasets, respectively. We conclude that our model has the potential to serve as a future benchmark for deep learning and network security research communities

Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

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Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. As a result, designing an automated detection system for citrus plant diseases is important. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this paper, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as Black spot, canker, scab, greening, and Melanose. The proposed CNN model extracts complementary discriminative features by integrating multiple layers. The CNN model was checked against many state-of-the-art deep learning models on the Citrus and PlantVillage datasets. The experimental results indicate that the CNN Model outperforms the competitors on a number of measurement metrics. The CNN Model has a test accuracy of 94.55 percent, making it a valuable decision support tool for farmers looking to classify citrus fruit/leaf diseases

A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

Brain cancer classification is an important step that depends on the physician's knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min–max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique

An Effective Bio-Signal-Based Driver Behavior Monitoring System Using a Generalized Deep Learning Approach

Recent years have seen increasing utilization of deep learning methods to analyze large collections of medical data and signals effectively in the Internet of Medical Things (IoMT) environment. Application of these methods to medical signals and images can help caregivers form proper decision-making. One of the important IoMT medical application areas includes aggressive driving behaviors to mitigate road incidents and crashes. Various IoMT-enabled body sensors or camera sensors can be utilized for real-time monitoring and detection of drivers' bio-signal status such as heart rate, blood pressure, and drivers' behaviors. However, it requires a lightweight detection module and a powerful training module with real-time storing and analysis of drivers' behaviors data from these medical devices to detect driving behaviors and provides instant feedback by the administrator for safety, gas emissions, and energy/fuel consumption. Therefore, in this paper, we propose a bio-signal-based system for real-time detection of aggressive driving behaviors using a deep convolutional neural network (DCNN) model with edge and cloud technologies. More precisely, the system consists of three modules, which are the driving behaviors detection module implemented on edge devices in the vehicle, the training module implemented in the cloud platform, and the analyzing module placed in the monitoring environment connected with a telecommunication network. The DCNN model of the proposed system is evaluated using a holdout test set of 30% on two different processed bio-signal datasets. These two processed bio-signal datasets are generated from our collected bio-signal dataset by using two different time windows and two different time steps. The experimental results show that the proposed DCNN model achieves 73.02% of validation accuracy on the processed dataset 1 and 79.15% of validation accuracy on the processed dataset 2. The results confirm the appropriateness and applicability of the proposed deep learning model for detecting driving aggressive behaviors using bio-signal data

Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security

The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution

An Efficient Game Theory-Based Power Control Algorithm for D2D Communication in 5G Networks

Device-to-Device (D2D) communication is one of the enabling technologies for 5G networks that support proximity-based service (ProSe) for wireless network communications. This paper proposes a power control algorithm based on the Nash equilibrium and game theory to eliminate the interference between the cellular user device and D2D links. This leads to reliable connectivity with minimal power consumption in wireless communication. The power control in D2D is modeled as a non-cooperative game. Each device is allowed to independently select and transmit its power to maximize (or minimize) user utility. The aim is to guide user devices to converge with the Nash equilibrium by establishing connectivity with network resources. The proposed algorithm with pricing factors is used for power consumption and reduces overall interference of D2Ds communication. The proposed algorithm is evaluated in terms of the energy efficiency of the average power consumption, the number of D2D communication, and the number of iterations. Besides, the algorithm has a relatively fast convergence with the Nash Equilibrium rate. It guarantees that the user devices can achieve their required Quality of Service (QoS) by adjusting the residual cost coefficient and residual energy factor. Simulation results show that the power control shows a significant reduction in power consumption that has been achieved by approximately 20% compared with algorithms in [11]

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used