A secure audio steganography approach

A wide range of steganography techniques has been described in this paper. Beside the evaluation of embedding parameters for the existing techniques, two problems -weaknesses- of substitution techniques are investigated which if they could be solved, the large capacity - strength- of substitution techniques would be practical. Furthermore, a novel, principled approach to resolve the problems is presented. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness

A Pre-Trained Ensemble Model for Breast Cancer Grade Detection Based on Small Datasets

Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers among women. Early detection of the cancer type is essential to help inform subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large datasets and are not developed for small datasets. Although large datasets may lead to more reliable results, their collecting and processing are challenging.  Materials and Methods: This paper proposes a new ensemble deep learning model for breast cancer grade detection based on small datasets. Our model uses some basic deep-learning classifiers to grade the breast tumors, including grades I, II, and III. Since none of the previous works focus on the datasets, including breast cancer grades, we have used a new dataset called Databiox to grade the breast cancers in the three grades. Databiox includes histopathological microscopy images from patients with invasive ductal carcinoma (IDC). Results: The performance of the model is evaluated based on the small dataset. We compare the proposed three-layer ensemble classifier with the most common single deep learning classifiers in terms of accuracy and loss. The experimental results show that the proposed model can improve the classification accuracy of the breast cancer grade compared to the other state-of-the-art single classifiers. Conclusion: The ensemble model can be also used for small datasets. In addition, they can improve the accuracy compared to the other models. This achievement is fundamental for the design of classification-based systems in computer-aided diagnosis

A novel approach for genetic audio watermarking

This paper presents a novel, principled approach to resolve the remained problems of substitution technique of audio watermarking. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well

Customized e-portfolio's features based on users requirements

According to the rapid pace of knowledge growth, universities try to improve their teaching method to enhance their student's knowledge level and apply some techniques to facilitate learning one of this new technique is e-portfolio. But Sometimes user's requirement as an important factor was ignored by designer and there is no motivation for students to use e-portfolio. The outcome of this study is a framework which e-portfolio expert could use as a guide line to design an e-portfolio toward user's requirement

Variables affecting the isage of e-portfolios by project managers in construction industry

The aim of this research is to discuss about the items which will affect to design e-portfolios based on construction Project manager’s requirements. Based on authors’ experiences and survey, some important items that would support eportfolios to be more effective and efficient has been specified. The result of this paper is a proposed method that can be used for designing new portfolios for project managers, especially in the field of construction industry. The questionnaire distributed among 38 project managers who were using e-portfolio in construction industry and their attitude for existing e-portfolio and also variable that affect usage of e-portfolio by project managers were asked. Based on the findings the most important factors that need to be considered to design an e-portfolio for construction project managers are: Motivating construction project managers by their organizations to use the e-portfolio, the ability of customizing e-portfolio by construction project managers, designing more effective e-portfolios that are more responsive to project managers’ enquiries, more user-friendly e-portfolios, considering project managers’ attitudes and preferences for designing e-portfolios, and finally updating e-portfolios based on workplace needs

Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method

Breast cancer’s high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks—EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50—integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system’s detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system’s superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes

NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization

For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research