Cardio-Vascular Disease (CVD) is one of the leading cause of death all over the world with expecting approximately 23.6 million individuals to be attacked by the CVD by 2030. Thus, the healthcare industry is trying to gather a large amount of CVD information, which can help the doctors to detect and identify the potential risk factors of the CVD. Deep learning can dig out the hidden pattern of the disease and symptoms from this structured and unstructured medical information. As a result, in this paper, we propose an algorithm to predict the risk factors of the CVD using the attention module based Long Short- Term Memory (LSTM), which has almost 95% accuracy and 0.90 Matthews Correlation Coefficient (MCC) scores; better than any other previously proposed methods. Moreover, we propose a novel Intelligent Healthcare Platform for continuous data collection and patient monitoring system. Initially, the proposed platform is used for data collection, and we find out the best suitable features from the dataset for applying various machine learning algorithms. The experimental results show that the attention module based LSTM outperforms than the other statistical machine learning algorithms for the prediction as well as indicates significant risk factors of the CVD, which can be supportive for the CVD patients to change their lifestyle.

The Software-Defined Network (SDN) is a next-generation network that uses OpenFlow to decouple the control plane from the data plane of forwarding devices. Other protocols for southbound interfaces include ForCES and POF. However, some security issues might be in action on the SDN, so that attackers can take control of the SDN control plane. Since live video calling, QoS control, high bandwidth needs, and resource management are inevitable in any SDN/Software-Defined Cellular Network (SDCN), traffic monitoring is an integral approach for safeguarding against DDoS, heavy hitters, and superspreaders. In such a scenario, SDN traffic measurement comes into action. Thus, we survey SDN traffic measurement solutions to assess how these solutions can make a secure, efficient, and robust SDN/SDCN architecture. This research classifies SDN traffic measurement solutions according to network application behavior and compares several ML approaches. Furthermore, we find out the challenges related to SDN/SDCN traffic measurement and the future scope of research, which will guide the design and development of more advanced traffic measurement solutions for a scalable, heterogeneous, hierarchical, and widely deployed SDN/SDCN architecture. In more detail, we list different kinds of practical machine learning (ML) approaches to analyze how we can improve traffic measurement performances. We conclude that using ML in SDN traffic measurement solutions will help secure SDNs/SDCNs in complementary ways.

Vision loss happens due to diabetic retinopathy (DR) in severe stages. Thus, an automatic detection method applied to diagnose DR in an earlier phase may help medical doctors to make better decisions. DR is considered one of the main risks, leading to blindness. Computer-Aided Diagnosis systems play an essential role in detecting features in fundus images. Fundus images may include blood vessels, exudates, micro-aneurysm, hemorrhages, and neovascularization. In this paper, our model combines automatic detection for the diabetic retinopathy classification with localization methods depending on weakly-supervised learning. The model has four stages; in stage one, various preprocessing techniques are applied to smooth the data set. In stage two, the network had gotten deeply to the optic disk segment for eliminating any exudate's false prediction because the exudates had the same color pixel as the optic disk. In stage three, the network is fed through training data to classify each label. Finally, the layers of the convolution neural network are re-edited, and used to localize the impact of DR on the patient's eye. The framework tackles the matching technique between two essential concepts where the classification problem depends on the supervised learning method. While the localization problem was obtained by the weakly supervised method. An additional layer known as weakly supervised sensitive heat map (WSSH) was added to detect the ROI of the lesion at a test accuracy of 98.65%, while comparing with Class Activation Map that involved weakly supervised technology achieved 0.954. The main purpose is to learn a representation that collect the central localization of discriminative features in a retina image. CNN-WSSH model is able to highlight decisive features in a single forward pass for getting the best detection of lesions.

Acute Lymphoblastic Leukemia (ALL) is a malignant neoplasm defined by the abnormal proliferation of immature lymphocytes in the hematopoietic system, specifically in the blood or bone marrow. The efficacy of ALL treatment is closely linked to its timely identification. Currently, the first diagnosis of ALL involves clinicians laboriously and fallibly examining stained blood smear microscopy images. Recently, deep learning techniques in biomedical diagnostics, focusing on human-centric approaches, have emerged as a potent tool to aid clinicians in their decision-making processes. As a result, researchers have devised a multitude of computer-aided diagnostic methods to detect ALL in blood images autonomously. However, most existing techniques for segmenting White Blood Cells (WBCs) do not consider the need for concurrent segmentation of the cytoplasm and nucleus. It is important to note that a significant drawback of the currently employed networks is their limited computational efficiency, which necessitates a substantial quantity of trainable parameters. The proposed deep learning model demonstrates favorable outcomes and can potentially be used to develop a dependable computer-aided detection system for leukemia malignancy. we suggest an Attention-Flipping Block (FAB) for the lightweight ALL-image segmentation model. It is evaluated using three publicly accessible datasets consisting of blood samples from individuals diagnosed with leukemia. These datasets are specifically referred to as C-NMC 2019, ALL_IDB1, and ALL_IDB2. With ALL-IDB2, the model’s segmentation accuracy is 93.56%, and its classification accuracy is 97.94 %, with an F1-Score of 97.65%.

The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC.