Proctor net: An AI framework for suspicious activity detection in online proctored examinations

Futuristic study of a criminal facial recognition: A open-source face image dataset

Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time. 
 

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Real-Time Detection of Face Mask Using Convolutional Neural Network

Received Apr 18, 2021 Revised Dec 16, 2021 Accepted Dec 30, 2021 The appearance of agile software development techniques (ASDT) since 2001 has encouraged many organizations to move to an agile approach. ASDT presents an opportunity for researchers and professionals, but it has many challenges as well. One of the most critical challenges is agile effort prediction. Hence, many studies have investigated agile software development cost estimation (ASDCE). The objective of this study is twofold: First, to propose an improved model based on support vector regression with radial bias function kernel (SVR-RBF) enhanced by the optimized artificial immune network (Optainet). Second, to perform a detailed comparative analysis of the proposed method compared to other existing optimization techniques in the literature and applied for ASDCE. The experimental evaluation was carried out by assessing the performance of the proposed method using some trusted measures like standardized accuracy (SA), mean absolute error (MAE), prediction at level p (Pred(p)), mean balanced relative error (MBRE), mean inverted balanced relative error (MIBRE), and logarithmic standard deviation (LSD). Throughout a dataset with 21 agile projects using the leave-one-out cross-validation (LOOCV) technique. The results obtained prove that the proposed model enhances the accuracy of the SVR-RBF model, and it outperforms the majority of existing models in the literature.

IAES International Journal of Artificial Intelligence (IJ-AI)

Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN)

Light-weight Cryptography is a subset of cryptography that aims to create interaction with low-power enabled devices. The use of constrained devices in most of the fields has created the need of light-weight cryptography. Many modern algorithms are designed to be used without hardware optimization tools as part of a computer software framework. This reality makes it difficult for devices with minimal computing capacity, small volumes, and low power consumption to use most current cryptographic algorithms. The foundation of light-weight cryptography has become cryptographic data protection techniques in low-cost systems. Light-weight cryptography is important in the case of the providing security to wireless sensor devices. Light-weight algorithm developers are sometimes forced to choose between two algorithm criteria, often mutually specific: protection, cost, and productivity. In practice, two of the three design goals are easy to optimize: protection and expense, while it is very challenging to simultaneously optimize all three design goals, safety and efficiency or cost and performance. Several Light-weight Cryptography algorithms are implemented in this respect: software and hardware. They have several and frequently contradictory features. In this regard two types of ciphers were used depending on the algorithm: one is block cipher and the other is stream cipher. In this paper, we are going to discuss the major role of Light-weight Cryptography in IoT devices and its challenges to run constrained devices in a specific Filed. As previously stated, the limitations of the research Lightweight Block Ciphers PRESENT, CLEFIA and Stream Ciphers Grain, Rabbit will be discussed.

Y. Kasiviswanadham

Papers

Criminal face identification system using deep learning algorithm multi-task cascade neural network (MTCNN)

The importance of light-weight encryption cipher in restricted IoT systems to make intelligent technology safer for devices