The brain neurons' electrical activities represented by Electroencephalogram (EEG) signals are the most common data for diagnosing Epilepsy seizure, which is considered a chronic nervous disorder that cannot be controlled medically using surgical operation or medications with more than 40 % of Epilepsy seizure case. With the progress and development of artificial intelligence and deep learning techniques, it becomes possible to detect these seizures over the observation of the non-stationary-dynamic EEG signals, which contain important information about the mental state of patients. This paper provides a concerted deep machine learning model consisting of two simultaneous techniques detecting the activity of epileptic seizures using EEG signals. The time-frequency image of EEG waves and EEG raw waves are used as input components for the convolution neural network (CNN) and recurrent neural network (RNN) with long- and short-term memory (LSTM). Two processing signal methods have been used, Short-Time Fourier Transform (STFT) and Continuous Wavelet Transformation (CWT), have been used for generating spectrogram and scalogram images with sizes of 77 × 75 and 32 × 32, respectively. The experimental results showed a detection accuracy of 99.57 %, 99.57 % using CWT Scalograms, and 99.26 %, 97.12 % using STFT spectrograms as CNN input for the Bonn University dataset and the CHB-MIT dataset, respectively. Thus, the proposed models provide the ability to detect epileptic seizures with high success compared to previous studies. 

Detection of epileptic seizure using EEG signals analysis based on deep learning techniques

The inexorable march of technological advancement, particularly within the digital domain, continues to exert a profound influence on global economies, societies, and governance frameworks. This paper delves into the intricate coordination between digital business and digital governance against the backdrop of sustainable development. By introducing an index system to gauge the levels of digital business and governance, this study assesses their coupling coordination using a coupling coordination model. Through this level of coordination, this paper assesses their respective contributions to the sustainable development objectives of EU countries through panel-corrected standard error (PCSE) estimates. The paper’s findings underscore several key conclusions: (1) Notable upswings are evident in the composite indices for digital business and digital governance growth. Among these, the index of digital business has demonstrated the most pronounced surge. Furthermore, digital business has experienced a distinct upward trajectory in recent years. (2) Although observable, the rise of the coupling degree is restrained, with an overall coupling degree that remains relatively low. The coupling progression has transitioned from a stage of low-degree coupling to that of primary coupling, with EU countries demonstrating fluctuating rising trends in their coupling degrees, marked by conspicuous regional disparities. (3) Over the examined period, the extent of coordination between digital business and digital governance substantially impacts the Sustainable Development Goals (SDG) index. Focusing on the interplay and harmonization between digital business and governance offers a novel pathway toward attaining the objectives of the Sustainable Development Goals.

The Coupling and Coordination Degree of Digital Business and Digital Governance in the Context of Sustainable Development

Breast cancer incidence has been soaring sharply, and this is causing grave concern worldwide due to its high mortality rates. It should be correctly diagnosed in the early stages in order to achieve better patient outcomes. Over the last decade, there has been a great demand for diagnosis systems based on AI that could be used in breast cancer detection and classification. These computerized devices utilize deep learning algorithms to analyze medical scans thereby allowing for subtle abnormality recognition and distinguishing malignant from benign tumors. Computer-aided diagnosis named CAD systems can assist radiologists and pathologists to be more precise with their diagnoses while at the same time increasing productivity. Furthermore, recent advances in CNN architectures coupled with attention mechanisms have further improved CAD systems for breast cancer diagnosis. Attention-based CNN models focus on crucial regions hence enhancing classification accuracy and reliability. In this study, we introduce a new approach that improves the classification of breast cancer using GoogLeNet architecture modified by an attention mechanism that is based on image regions. The modified GoogLeNet has a spatial transformer network (STN), which allows it to focus on significant areas in breast histopathology images using selective attention. Through the attention mechanism, the model becomes better at learning discriminatory features that indicate different subtypes of breast cancer. In order to evaluate the effectiveness of this method, we implemented experiments using the BreaKHis dataset for classifying breast carcinomas. This dataset has been intentionally collected under various magnifications so as to facilitate binary and multiple classification tasks. These outcomes clearly show that modified GoogLeNet with attention outperforms the original GoogLeNet architecture in terms of accuracy. For binary classification, the proposed model demonstrated an accuracy rate of 98.08%, whereas GoogLeNet’s rate was 94.99%. For multi-class classification, at 100x magnification, this model achieved an accuracy of 94.63% while the accuracy of the original GoogLeNet was 85.06%. It is evident from these findings that the efficiency of breast cancer diagnosis significantly improved under this proposed approach. The findings of the study show that incorporating the modified GoogLeNet framework with an attention mechanism in CAD systems for breast cancer detection can improve their performance. Combining deep learning and attention models together can lead to more accurate treatment decisions and better patient results. More efforts are needed to further develop CAD systems in this area to assist ongoing endeavors towards upgrading them and ultimately contribute to saving many lives in the fight against breast cancer. 

pdf/enhancing-breast-cancer-classification-using-a-modified-37b8yxsjt0.pdf

Enhancing Breast Cancer Classification using a Modified GoogLeNet Architecture with Attention Mechanism

Breast Cancer (BC) is a killing disorder, every year it kills millions of human beings. Early diagnosis is the only way to mitigate the mortality rate. Among all kinds of screening methods, medical imaging is an essential method for screening BC. Existing medical imaging alters the tissue structure and cell morphology. To overcome these limitations, histopathology image is used because it can support the decision of pathologists about the closeness or the non-appearance of a disease, as well as it can help in infection development estimation. Hence, this research develops an efficient method for BC classification using the proposed Adam Golden Search Optimization-based Deep Convolutional Neural Network (AGSO-DCNN). Initially, Gaussian filter-enabled pre-processing is utilized for mitigating the noises composed in the input images. Afterwards, k-means clustering is used to feed the input images into the segmentation phase to reduce the complexity of the image. Then, to extract features like shape features, statistical features, Local Vector Patterns (LVP), and Pyramid Histogram of Oriented Gradients (PHOG) feature extraction is performed. Thereafter, the obtained features are forwarded to the multi-grade BC classification stage, where DCNN is employed for classifying the image into six categories, such as apoptosis, tubule, mitosis, non-tubule, tumour nuclei, and non-tumor nuclei. DCNN is trained by the formulated AGSO mechanism, which is obtained by incorporating the Adam Optimizer and Golden Search Optimization (GSO) algorithm. Moreover, the AGSO-based DCNN technique achieved better accuracy, TPR and TNR with the values of 97.90%, 98.00%, and 98.30%, respectively. 

Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological image

<title>Abstract</title> Breast cancer is a significant health concern globally, emphasizing the need for accurate diagnostic tools for early detection and treatment. In this research, we propose a novel methodology leveraging deep learning techniques for classifying breast histopathology images as benign or malignant. We employ state of the art convolutional neural networks (CNNs), including VGG16, VGG19, ResNet50, ResNet101, and a custom CNN architecture, to extract discriminative features from histopathology images. One of the key contributions of this work is the incorporation of class weights into the training process, aiming to address class imbalance in the da-taset and enhance model performance. We evaluate the efficacy of our approach using various performance metrics, including accuracy, precision, recall, and F1-score, on the publicly available BreaKHis dataset. 

Leveraging Deep Learning for Breast Cancer Detection : An Comprehensive study

The most effective and feasible method for treating cancer is early diagnosis of breast cancer. An appropriate software tool, known as computer-aided diagnosis, helps doctors identify or diagnose cancer in the early stages and more quickly, hence decreasing the mortality rate. Convolution neural networks (CNNs) have lately been utilized in medical image analysis, which can discover cancer cells or classify histopathology images by processing a large amounts of training data. In this study, we carried out transfer learning (TL) accompanied with a new self-learning algorithm for classification of breast cancer by processing the histopathological images. In this study, several pre-trained deep neural networks including, Inception V3 Net, VGG19, Alex net, ResNet-18, Google net, Shefflenet, Mobile net, Resnet 101, Inception ResnetV2 Net, and Squeeze net were examined. Using a self-learning technique for classification of sub-images instead of the whole image can increase the accuracy of cancer classification. In this technique, the main issue is that the true label of sub-images is unknown, and the classifier should be trained using noisy labels. Therefore, a hierarchal self-learning method is utilized to correct the false labels gradually. The main rule for correcting the false labels is designed based on some prior knowledge about the errors exist in the initial labels of sub-images. The proposed self-learning method reached the accuracy of 99.1%, after 4 level of label correction process accompanied with Inception-V3Net pretrained network. Overall, the use of self-learning DNNs for the classification of histopathological breast cancer images can improve the accuracy, efficiency, interpretability of the classification results and contribute to the development of personalized and precision medicine. 

A self-learning deep neural network for classification of breast histopathological images

The public sector, private firms, the business community, international trade organizations, international organizations, and civil society are generating, sharing, and utilizing big data that is high in volume, veracity, and velocity and comes from diverse sources. They pursue big data as "new oil" and adopt data-centric trade policies to promote a data-driven economy and to ensure the extensive application of the UN 2030 Sustainable Development Goals. Data and information have long been vital elements of the digital economy. However, there is a disparity between advanced and developing countries' communities' ability to access, collect, integrate, store, analyze, and use data to create commercial value, promote digital economy, and resolve social and environmental challenges. From the above, the term 'data divide' emerges. Despite stakeholders' vast interest and efforts, the data divide is still a key concern and severely impacts developing countries' digital economies. In this study, through a systematic literature review, we identify problems regarding the data divide in digital trade and offer proposals to the stakeholders to tackle the data divide in digital trade through data policies and holistic trade regulatory frameworks. In this manner, we contribute to the ongoing discussion around the data divide in digital trade and its impacts on the digital economy.

Data divide in digital trade, and its impacts on the digital economy: A literature review

End of 2019 had seen global spread of the deadly coronavirus (SARS-CoV-2) pandemic, which kills people, puts a large portion of the world in danger, and poses a serious threat to all of the world's nations. Leading medical professionals are working extremely hard to identify the virus, develop treatments for it, and create the vaccines that are required to stop and limit its spread. This study intends to develop a low-cost electronic health system to observe patients with covid infections and lessen the work required of clinicians. An enhanced approach for remote health monitoring in hospitals or detention facilities is offered by the internet of things (IoT). The IoT keeps and displays the patient's medical data via a web browser or through specialized apps that offer remote treatment once the sensors collect it. When a patient is in danger, the system offers immediate action to send alarms by email and SMS and to rapidly provide drugs to the patient. Doctors will be updated on each patient's condition thanks to this message.

Developing and Implementing an Iot Managed by Electronic Devices for Covid Patient Monitoring via a Secured Communication System

Cutting-edge cnn approaches for breast histopathological classification: the impact of spatial attention mechanisms

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