Effect of Microwave Annealing on the Structure and Magnetic Properties of Co-based Amorphous Ribbons

We investigated the structure and magnetic properties of CoFeSiB amorphous ribbons in the presence of microwave emission. Samples have been emitted onto in the microwave cavity with working frequency of 2.45 GHz at different times of emission. Structure and surface properties of samples emitted to have been investigated by X-ray diffraction and Atomic force microscopy. Also, magnetic properties of samples were investigated by magneto-optical Kerr effect measurement. Our results show that increasing in annealing time may change the crystalline state of samples and also decrease the magnetic coercivity and surface roughness.     

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this paper, we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning (RMDL) which uses the association approaches of multiDL to yield random models for classification. We present a methodology for using Generative Adversarial Networks (GANs) to generate images for data augmenting. Through experiments, we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency. Experimenting across both MNIST and CIAFAR-10 datasets show that, error rate with proposed approach has been decreased with different random models.     

A Secure and Lightweight Chaos Based Image Encryption Scheme

In this paper, we present an image encryption scheme based on the multi-stage chaos-based image encryption algorithm. The method works on the principle of confusion and diffusion. The proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing schemes. Initially, an image (red, green, and blue components) is partitioned into blocks with an equal number of pixels. Each block is then processed with Tinkerbell Chaotic Map (TBCM) to get shuffled pixels and shuffled blocks. Composite Fractal Function (CFF) change the value of pixels of each color component (layer) to obtain a random sequence. Through the obtained random sequence, three layers of plain image are encrypted. Finally, with each encrypted layer, Brownian Particles (BP) are XORed that added an extra layer of security. The experimental tests including a number of statistical tests validated the security of the presented scheme. The results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature.     

The Adoption of Electronic Procurement in Singapore

The emergence of new Internet technologies has far-reaching ramifications on the way business is conducted. Notably, it has given rise to e-commerce, which is the transaction of business via the Internet. Recognizing the importance of the Internet as a powerful business tool, many Asian companies have moved quickly to take advantage of electronic commerce. This study explores the role of business-to-business e-commerce, with an emphasis on e-procurement among companies in Singapore. It examines the impact of Internet-based technology on the buyer side of the procurement function, how e-procurement is helping organizations to enhance their competitiveness and the challenges to its adoption. The study also presents the findings from a survey conducted to gain insight into the adoption of e-procurement among companies in Singapore. Specifically, it seeks to uncover the prevalence of and extent to which companies in Singapore are adopting e-procurement, the benefits reaped and the barriers encountered in the course of implementation.     

Dates Fruit Recognition: From Classical Fusion to Deep Learning

There are over 200 different varieties of dates fruit in the world. Interestingly, every single type has some very specific features that differ from the others. In recent years, sorting, separating, and arranging in automated industries, in fruits businesses, and more specifically in dates businesses have inspired many research dimensions. In this regard, this paper focuses on the detection and recognition of dates using computer vision and machine learning. Our experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates fruit. Classical machine learning includes the Bayesian network, Support Vector Machine, Random Forest, and Multi-Layer Perceptron (MLP), while the Convolutional Neural Network is used for the deep learning set. The feature set includes Color Layout features, Fuzzy Color and Texture Histogram, Gabor filtering, and the Pyramid Histogram of the Oriented Gradients. The fusion of various features is also extensively explored in this paper. The MLP achieves the highest detection performance with an F-measure of 0.938. Moreover, deep learning shows better accuracy than the classical machine learning algorithms. In fact, deep learning got 2% more accurate results as compared to the MLP and the Random forest. We also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.     

Are We Ready to Innovate? Work Climate‐Readiness to Innovate Relationship: The Case of Jordan

This paper aims at understanding the role of work climate in influencing employees’ innovation. The study consists of two core concepts: work climate and readiness to innovate. By surveying a sample of 1000 employees from 20 industrial companies in Jordan, it investigates the impact of work climate on employees’ readiness to innovate. The results reveal that employees’ perceptions of their work climate tend to play a significant role in their readiness to innovate, with implications for both practitioners and researchers. An interesting finding is that older employees showed higher climate satisfaction reports than did younger employees, and were found to be more innovative. In addition, male employees reported higher levels of readiness to innovate – as rated by themselves – than did their females counterparts.     

Usability and Security of Arabic Text-based CAPTCHA Using Visual Cryptography

Recently, with the spread of online services involving websites, attackers have the opportunity to expose these services to malicious actions. To protect these services, A Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is a proposed technique. Since many Arabic countries have developed their online services in Arabic, Arabic text-based CAPTCHA has been introduced to improve the usability for their users. Moreover, there exist a visual cryptography (VC) technique which can be exploited in order to enhance the security of text-based CAPTCHA by encrypting a CAPTCHA image into two shares and decrypting it by asking the user to stack them on each other. However, as yet, the implementation of this technique with regard to Arabic text-based CAPTCHA has not been carried out. Therefore, this paper aims to implement an Arabic printed and handwritten text-based CAPTCHA scheme based on the VC technique. To evaluate this scheme, experimental studies are conducted, and the results show that the implemented scheme offers a reasonable security and usability levels with text-based CAPTCHA itself.     

VacChain: A Blockchain-Based EMR System to Manage Child Vaccination Records

The digitalization of healthcare-related information service systems has become a trend across the world. However, several crucial services are still provided manually due to a lack of trust in digital solutions. One such service is keeping records of children’s vaccination, which still relies on a paper-based file system in most parts of the world. This approach causes serious data integrity problems. Recently, healthcare has become a potential application area of the blockchain, as it can preserve and protect highly sensitive private medical records while sharing these records in a decentralized manner without losing personal ownership. Therefore, we propose a new digital model to track a child’s vaccination records using blockchain. In particular, this proposed application helps improve the vaccination record-keeping process by ensuring the integrity of the preserved data in a more secure way. In an emerging pandemic situation, our approach can be extended to manage the overall vaccination process effectively.