Reviewerly/exHarmony huggingface dataset:
Breast Tumor Detection In Mammogram Images Using Convolutional Neural Networks

Breast cancer is the second leading cause of death among women. Mammogram images are the widely utilized method to identify breast cancer at an early stage. In this study, we implemented a convolutional neural network that classifies mammogram images into normal and abnormal(tumor) with 100% accuracy. The dataset was collected from the Mammographic Image Analysis Society MiniMammographic Database (MiniMIAS) and due to a shortage of abnormal mammography, 92 images were added from the Chinese Mammography Database (CMMD), which only contains abnormal mammogram images. The dataset was pre-processed using several filters in order to extract the ROI (Region Of Interest) and eliminate any noises, resulting in better images for training, which were shown to be effective based on the results. The dataset was split into 75%, 5%, and 20% as training, validation, and testing sets respectively. The proposed model was trained, then evaluated using a test set with 100% accuracy.
Gaussian Denoising By Time Domain and Frequency Domain Filters for MRI Brain Images

Image restoration is the study of recovering the undistorted image. In this study, Time Domain (TD) filters and Frequency Domain (FD) filters are designed to discover how good a restoration of Magnetic Resonance Image (MRI) of brain is possible. The main property of image restoration by filters is that the edges in the image should be preserved while denoising the image. At first, the Gaussian noise is generated with zero mean and different variances. Then, the system generated Gaussian noise is added to the 256 by 256 pixel MRI brain images in the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database images. Then the noisy images are denoised by the TD and FD filters. The results of processing images using FD filter show an improvement over the TD filters in terms of Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR). This is because the FD filter provides the details of high frequency low frequency components effectively than TD filters.
Temporally Consistent Video Manipulation for Facial Expression Transfer

Facial expression transfer is an important problem in the computer vision community due to its applicability in human-computer interaction, facial animation, etc. Most of the existing approaches mainly deal with images. However, expression transfer to videos is yet to be explored. The extension of existing image-based approaches to videos often leads to flickering artifacts in the result. This is due to the temporal inconsistency that may have been produced due to the independent processing of frames. In this paper, we address this problem by proposing a combination of loss functions in association with a deep learning-based method. Image reconstruction loss takes care of spatial domain consistency. The flow and warp losses address the temporal consistency. By maintaining consistency spatially as well as temporally, our method produces visually plausible results. The efficacy of our method is further explored in different problems like video dehazing, and video colorization.
Bibliometric study about chaotic cryptography in developing countries

In this paper, an analyse of scientific journal articles related to chaotic cryptography from 2015 to 2021, is presented. In doing so, the study uses the Web of Science Core Collection database to analyze the data. ‘biblioshiny’ a web-interface of the ‘bibliometrix 3.0’ package of R-studio has been deployed to conduct bibliometric analysis. Also, a graphical mapping of the bibliometric material by using the visualization of similarities (VOS) viewer software, was developed. The study relies on the proposed approach to explore the influence of developing countries on Chaos-based encryption. We found out that developing countries are the most active on chaotic cryptography.
An improved KPLS-KELM method for breast cancer detection

The conception of a Computer-Aided Diagnosis system (CAD) using Artificial Intelligence (AI) is a hot topic in the domain of medical diagnosis. Recently, many approaches have been developed. In the proposed work, a novel classification technique from mammograms based on Kernel Extreme Learning Machine (KELM) and Kernel Partial Least Square (KPLS) method is introduced. The suggested algorithm first used the KPLS algorithm to extract features from the images. The extracted characteristics were then sent to the KELM classifier. In order to improve the generalization of the proposed approach, the cross-validation strategy was used. The simulation results were tested on the Mammographic Image Analysis Society (MIAS) dataset and measured using accuracy, F score, sensitivity, and specificity analysis. These results were compared to existing approaches tested on the same dataset and it was observed that the proposed work is the most efficient.
Demographic Attributes Classification via Convolutional Neural Networks: A Proposed Solution

Identifying age, gender, and ethnicity of a person is still a difficult undertaking, given the absence of a specific definition of ethnicity, the wide variety of age distributions and the various aging factors which primarily affects the facial traints. Therefore, this paper propose four deep learning models which extract and classify the age, gender, ethnicity and the entire set of demographic attributes at the same time as well as two data sampling methods to deal with the imbalanced nature of the chosen dataset. The accuracy for the proposed gender, ethnicity and the multi-label models were 96.14%, 88.72% and 93.09% respectively and were trained and tested using the UTK-Face dataset.
Traffic Sign recognition for smart vehicles based on lightweight CNN implementation on mobile devices

The concept of smart vehicles is becoming an essential feature that ensures driver comfort and security. Smart vehicles are equipped with intelligent systems based on advanced technologies that perform a set of tasks for the mentioned purposes. Recognizing Traffic sign is one the most important systems that guarantee a high-security level. However, it is difficult to develop the best traffic sign recognition system due to numerous obstacles. such as weather conditions, geometric deformation, and most important is the material limitation. In this work, we proposed the implementation of a lightweight convolutional neural network (CNN) model on a mobile device to overcome the mentioned challenges. The proposed CNN combines high performances and low computation complexity. Evaluating the proposed model on publicly available datasets proved its efficiency. Besides, the implementation of the CNN model on the pynq platform demonstrates the possibility of using a wide range of mobile devices for the inference of the proposed model.
Adaptive Diffusion Based Restoration for Noisy Facial Image Recognition

Identity recognition from corrupted face image remains difficult, since noise can seriously affect the image quality. Thus, it is necessary to enhance facial image before starting the recognition process. In this paper, we extract relevant features from the noisy facial image using PCA decomposition that seperates the small features from the large ones. For restoring these features, we apply an adaptive diffusion method based on eigenratio of the vectors that represent each feature. Therefore, the denoising process is adapted according to region caracteristics where the small features are enhanced by shock of backward diffusion filter and the large features are smoothed with isotropic diffusion.We have used the ORL database and three different types of noise: Gaussian, uniform and salt-pepper. The proposed method tests six classifiers to find the best one. Numerical experiments show that the proposed method gives the best results for the tested noise types in terms of objective metrics, PSNR and SSIM. They show that SVM classifier provides good performance and outperforms other classifiers with the highest accuracy of 97.85%.
Hybrid Ensemble Model for Handling Class Imbalance Problem in Big Data Analytics

In big data analytics, class imbalance problem adversely affects the performance measure of machine learning classifiers as they tend to achieve higher accuracy by giving more attention to the instances from the majority class, thus ignoring those from the minority class, which are often the class of interest and of value. Techniques for handling class imbalance include; data level, algorithm level, and ensemble techniques. The ensemble models are seen to always perform better as compared to the other techniques. In our work we propose an ensemble model based on random oversampling and bagging models for handling class imbalance. Additionally, the genetic algorithm technique has been adopted for feature selection, and the random forest and decision trees are used as machine learning classifiers. The experimental results showed that our ROS-Bagging ensemble model outperformed ROS and bagging separately to handle class imbalance. There was steady rise in precision value for all the classifiers used when trained using a dataset balanced using ROSBagging ensemble model.
An Improved Security Approach for Medical Images and Patients’ Information Transmission

Cryptographic functions involve protecting personal information transmitted over the network against destructive attacks. The objective of this paper is to reinforce the safety of the transmission and the storage of medical images by using a hybrid framework for medical image encryption and authentication. The proposed model is based on symmetric and asymmetric approaches. To evaluate the strength of the proposed approach; our cryptosystem is tested by various tools. The experimental findings prove that our cryptographic system provides high robustness that can resist the most known cryptanalysis attacks.
Analysing ICT Initiatives towards Smart Policing to Assist African Law Enforcement in Combating Cybercrimes

The Information and Communication Technology (ICT) revolution has become a critical component of every law enforcement when combating crimes. With the surge of cybercrimes, it has increasing pressure on law enforcement to guarantee that cybercrimes are successfully combated. To combat cybercrimes, there are initiatives such as Social Media, Open Data, Geographic Information System Crime Mapping, Science of Security (SoS), Electronic Safe for Kid, Cybersecurity Competition, Cybercrime Reporting Portals, and Artificial Intelligence solutions which are vital to law enforcement in deterring more cybercrimes. This study employed a systematic literature review to look at relevant ICT initiative at the disposal to African law enforcement and they can be of assist in combating cybercrimes.
Using population algorithms to optimize the objective function when training artificial neural networks

The use of population algorithms for artificial neural networks (ANN) is in high demand in our time, especially with the spread and implementation of artificial intelligence systems, and because conventional teaching methods in some cases no longer provide optimal speed and accurate results. This paper compares different methods for training a neural network, namely: genetic algorithm, gradient descent (full-batch, mini-batch, stochastic), Adam, Nesterov accelerated gradient, harmonic search, bacterial foraging optimization. A comparison of methods is made according to the criteria of loss function and convergence time. For the constructed classifiers, the accuracy, recall, and F-measure are calculated. The conducted experiments showed that according to the criterion of convergence and the time spent, the genetic algorithm is better than the standard, stochastic, and mini-batch backpropagation method. In addition, population algorithms turned out to be faster than standard gradient descent methods (and their variations); however, they lose in speed and accuracy to the adaptive moment estimation method.
New Method for Earth Fault Localization using the Hilbert Transform

In this paper the architecture of a software prototype of earth fault localization is explained and the results of a sensitivity analysis of the elements of digital models of medium voltage grids are shown and how these results affect earth fault localization. Eight grid elements were studied in the sensitivity analysis: primary transformer, secondary transformers, cables, loads, synchronous machine generators, infeeders, leakage capacitances and a private grid with lack of information of cables. The cases of including leakage capacitances and synchronous machine generators are interesting for the field of electromagnetic transients in power systems. Tables with the summary of the results with the minimum, average and variance of the correlation between the generated fault signatures with the models of the cases of the sensitivity analysis and with the reference models are presented. Based on the average correlation results, ranges associated to a low, medium and high impact level were defined. One case of every impact level was used to evaluate the preprocessing step of the Hilbert transform for earth fault localization with correlation, which is a new and effective method and therefore an important contribution to the field of earth fault localization in medium voltage grids.
Building IoT sensors to estimate PM<sub>2.5</sub> concentrations

Air pollution is getting worse now, and suspended particles are also increasing. With the progress of technology and sensing technology, small and low-cost sensing platforms appear one after another. However, if small sensors require a large number of arrangements, it is still a big burden. In this study, this research use Camera Module V2 image sensor to capture images, and the image features are found by image processing. Based on the Raspberry Pi v4 test platform, the image sensor set up outdoors to shots the estimated area in fixed time, and stored the image in MySQL for use of data, using crawler to capture the feature of the weather in the monitoring station, extracting feature values from the collected images, and input the feature values into support vector regression (SVR) to estimate PM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</inf> concentration. The result of experiment shows that the image cannot be characterized by the visibility because the distance between the monitor and building is not enough.
Performance evaluation by simulation of the synchronous course transcription system in the ad hoc network of the DOUNG (Digital Open Universities - New Generation)

Recent work brings to the DOUNG distance education architecture evolution by building a classroom ad hoc network including learner electronic devices such as computers, laptops and telephone cells. After evaluating various routing protocols, the problem of data loss is resolved through the transmission of the blackboard image for a good period of time. The content remains static for this duration and is followed by the voice of the teacher. In this paper, a new solution to overcome the data loss issue is suggested with a transcription chain and a consistency checker in the model of dictation. For the lecture delivery alive, the new system converts the audio stream into text format which consistency is verify before crossing the network. The learner uses the ATRS (Advanced Text Reading System) to convert the text into audio. A comparison of the two methods is conduct under intuitive and mathematical models before running simulations in the ns2 environment
Explainability Analysis of a Machine Learning Model for Industrial Applications

Being able to explain the predictions generated by Machine Learning models is becoming increasingly necessary for numerous applications. It not only validates that the model behaves as expected but also allows for detailed analyses of the results that generate insights about the input data and the application itself. Despite its popularity in the academia, the adoption of such methods, and artificial intelligence itself, in the manufacturing industry is still in its infancy, mostly due to lack of professional expertise and the usage of antiquated technology. In this paper, we train a regression model that predicts the yield value of seamless steel tubes at the end of the heat treatment process. We then use Tree SHAP, a state-of-the-art model explainability framework, to analyze our model. We perform a detailed analysis of several explainability plots that uncovers non-trivial knowledge regarding the heat treatment process and the model itself. Our use case highlights the gains of machine learning explainability analysis in industrial applications, which we hope encourages the adoption of this type of technology in the industry.
Blind Digital Watermarking Method Based on Quadtree Decomposition and Complex Wavelet Transform for Medical Application

A digital watermarking scheme based on dual-tree complex wavelet transform (DTCWT), and the quadtree (QR) is suggested in this paper, in this technique the DTCWT is exerted on the host image as a first step in the process, then decomposing the low frequencies using the quadtree which illustrates the data structure, to obtain a suitable position for the watermark insertion, once the decomposition is done, the watermark is incorporated in the consequent block of the quadtree using a blind embedding function based on quantization. At the extraction phase, the watermarked image is treated similarly to the embedding phase, then an extraction formula based is applied to retrieve the watermark. The experiments had shown the superior results achieved in terms of high fidelity and similitude, furthermore, a comparison was effectuated as well as to prove the suggested technique’s effectiveness.
A New Spectral Intrinsic Decomposition based Image Inpainting Method

In this paper, an inpainting approach derived from a multiscale and adaptive analysis is proposed. The proposed method use some pixels as characteristics points of the Spectral Intrinsic Decomposition (SID) of inputed image and compute missing pixels by the recomposed image of components witch eigenvalues are equal to one. The recomposition is driven by a diffusivity function. The inpainting method is implemented and applied to well known images data base. The results shows the effectiveness of the new method by reducing considerably noise and gives promising results for image restoration.
Emotion, Age and Gender Recognition using SURF, BRISK, M-SVM and Modified CNN

Nowadays, it is a challenging task of recognizing the emotions, gender, and age of a person at the same time. Researchers are working hard to develop techniques and code to extract features from faces. Different types of architecture are already exploited using deep learning to get better outcomes. The main goal of this research is to identify the emotions, gender, and age of children, men, women, and common gender of different ages together which gives better results than the current systems. In this research, we proposed a methodology using SURF and BRISK and Modified Convolutional Neural Network (M-CNN) model to extract features where classification is followed by a Multi-support Vector Machine (M-SVM). In our prediction part, we use the head detection technique instead of face detection to get a better outcome. We created a features dataset that will match the features of the test image and will detect the head from the test image. We created three datasets named Face Emotion Recognition (FER-2022) dataset, Gender Recognition (GR-2022) dataset and Age Recognition (AR-2022) dataset respectively which applied to evaluate the perfection of the proposed system. Where the accuracy of classification of emotion, gender and age are 95.45% 99.25% and 95.69% respectively.
Invasive Region Segmentation using Pre-trained UNet and Prognosis Analysis of Breast Cancer based on Tumor-Stroma Ratio

Breast cancer (BCa) is a type of disease that has multiple prognostic markers that differs from one cancer stage to another. Assessing the area and pattern of cancer regions is essential for pathological investigations. However, the main purpose of this study is to segment the regions of invasive carcinoma (i.e., non-tubular and tubular) in the histological sections of BCa. The segmentation was performed on hematoxylin and eosin (H&E)-stained tissue slides of 42 BCa patients from 5 different centers in Korea. The tumor-stroma ratio (TSR) is a promising prognostic parameter for BCa as well as in other epithelial cancer types estimating the area of stroma and tumor regions. Therefore, in this paper, we used the pre-trained convolutional neural network (CNN) models as a backbone of UNet, to precisely extract the tumor regions from the stroma tissue components for TSR analysis.
Leveraging advancements of eLearning technology for more accessibility for student learning

In higher education, change remains a constant phenomenon, from the reinvention of classrooms to the shift in roles as information and content become easier to access. One of the changes that lay at the core of these changes is the pervasive nature of technology in learning. Universities and other higher education institutions are adopting new learning technologies to meet the needs of twenty-first-century students and to develop new teaching approaches. This study contributes to a growing body of research that focuses on utilizing improvements in eLearning technology for greater accessibility to student learning by exploring the possibilities of future learning technologies. The study demonstrates how the Business Analysis Core Concept Model, a business analysis conceptual framework will be applied to perform a superior analysis in assessing the current e-learning model and the proposed augmented reality system. Furthermore, use cases were created to demonstrate the customer journey mapping with the current e-learning system and the proposed system for leveraging the advancement of eLearning technology to improve student learning accessibility.
A Customized Python Interface for Windows OS for a Low Budget USB-to-CAN-Adapter

Over the last three decades, the Controller Area Network (CAN) has become the dominant communication in embedded systems. Especially for automotive systems it offers advantages including high robustness, low error rate and high reliability combined with low power consumption. Therefore, learning the basics of this bus system is substantial in this field. Nowadays, various media about the functionality and use of CAN exist which make it easy to read into the topic. But often, theory alone is not sufficient. To deepen the understanding, practical implementation contributes significantly. However, affordable and easy-to-use CAN devices for training purposes are scarce. Existing equipment can be divided into expensive professional devices, which have many functions and inexpensive ones for hobbyists, which require difficult configurations. Therefore, a practical solution is a low-budget device equipped with an overlay which deals with the time consuming configurations. This paper covers the development of a python interface for a purchasable cost effective CAN device for Windows OS. The intention is to create an easy-to-use program that enables beginners to get in touch with CAN and collect practical experience. At the start, a brief explanation of the CAN functionality is given. After that, we introduce the hardware used in this project. Next, the software part covers the development of the interface and the integration of this interface into python-can. Furthermore, a virtual playground is introduced for testing purposes. Also, to demonstrate the functionality of the interface, a test program is executed in conjunction with a logic analyzer.