Owing to the latest advancements in networking devices and functionalities, there is a need to build future intelligent networks that provide intellectualization, activation, and customization. Software-defined networks (SDN) are one of the latest and most trusted technologies that provide a method of network management that provides network virtualization. Although traditional networks still have a strong presence in the industry, software-defined networks have begun to replace them at faster rates. When network technologies emerge at a steady rate, SDN will be implemented at higher rates in the upcoming years in all fields. Although SDN technology removes the complexity of tying control and data plane together over traditional networks, certain aspects such as security, controllability, and economy of network resources are vulnerable. Among these aspects, security is one of the main concerns that are to be viewed seriously as far as the applications of SDN are concerned. This paper presents the most recent security issues SDN environment followed by preventive mechanisms. This study focuses on Internet control message protocol (ICMP) attacks in SDN networks. This study proposes a security policy protocol (SPP) to detect attacks that target devices such as switches and the SDN controller in the SDN networks. The mechanism is based on ICMP attacks, which are the main source of flooding attacks in the SDN networks. The proposed model focuses on two aspects: security policy process verification and client authentication verification. Experimental results shows that the proposed model can effectively defend against flooding attacks in SDN network environments.

Vehicular ad hoc networks (VANETs) are a rapidly evolving field at the intersection of intelligent transportation systems, emphasizing the need for a stable and scalable VANET topology to accommodate growing vehicular densities. The intricate challenge of route selection calls for advanced clustering protocols to bolster road safety and message routing. This research introduces a novel approach to intelligent clustering routing protocols, leveraging heuristic-based solutions built upon an enhanced ant colony optimizer (ACO) framework. The study unfolds in two stages: the creation of a dynamic search space model and the election of cluster heads (CHs). The innovative dynamic aware transmission range parallel Euclidean distance (DA-TRPED) technique establishes a dynamic search space using the parallel Euclidean distance (PED) concept. This approach evaluates vehicular nodes by estimating PED values, reducing the search process’s complexity. Subsequently, an intelligent cluster head is selected by enhancing the dynamic evaporation factor (DEF) within the ACO technique. The experimental validation of the DA-TRPED technique takes place in NS2 simulations, demonstrating superior performance compared to conventional ACO. This enhancement is evident in metrics such as packet delivery, packet drop, throughput, end-to-end delay, and the lifetime analysis of clustered nodes. The proposed approach holds promise for optimizing VANETs, enhancing their stability and scalability while promoting road safety and efficient message routing.

Stroke-related diseases are rapidly increasing day by day due to the changes in environmental factors including lifestyles, food habits, and stress-related working cultures. According to a recent report from World Health Organization (WHO), Stroke is the second largest disease after cardiovascular disease that leads to death. Early diagnosis of stroke-related diseases was one of the major requirements for patients as well as medical professionals. Deep learning techniques are one of those methods that are suitable for stroke disease diagnosis when deployed properly. This research proposed a medical framework approach for the detection of abnormalities in the ECG data related to stroke diseases. ECG plays a vital role in the detection of several stroke risk factors including left ventricular hypertrophy. This work proposed a framework based on Long Short-term Memory (LSTM) network for predicting stroke-related diseases with ECG data and other parameters. The experimental results show that 90% accuracy results with the combination of ECG data and the Deep learning approach. Finally, Receiver operating characteristic (ROC) curves has shown promising results. This work also proved that the model is suitable for the early detection of stroke-related diseases with minimum overhead in terms of efficiency.

News article are the source of historic information that can be accessed whenever required. Today in modern world, Internet is also doing the same task and full of online news on multiple topics which also contains some irrelevant or unhelpful information. People are living a very busy and hectic life and they does not have enough time to read the complete news article. They want to read information as per their area of interest. Therefore, it becomes important to process online news content and classify them into various categories like sports, entertainment, Business, Finance etc. so that it become easily and quicky accessible to the user. This paper is an attempt in the direction of classifying the news text into different sections using Machine Learning algorithms. Experiment is performed on an online dataset taken from Kaggle. This dataset was having around 210K news headlines from 2012 to 2022. Four different classification algorithms (Random Forest, Decision Tree, K-Neighbor Classifier and Gaussian NB) are applied on the dataset to find the results.

Satellite image water body identification is an important part of scientific progress. Because of factors such as aquatic vegetation, the unique lake or river hues, sediments along the bank, and so on, it can be difficult to determine where one body of water ends and another begins. To improve the process of extracting water bodies from satellite photos, both the variety of characteristics and the information they provide about their meaning need to be expanded. It may be utilized in a variety of contexts, such as forecasting the occurrence of natural catastrophes and identifying the presence of drought and flooding. As it relates to the study of remote sensing and image interpretation, automatically extracting water bodies from many satellite images with varying complexity targets is a crucial and challenging job. Mostly in the area of semantic segmentation of remote sensing images, convolutional neural networks, often known as CNNs, have emerged as a popular option in recent years. When it comes to executing water body segmentation, however, generic CNN models face a number of challenges. In this paper, in order to locate the bodies of water, we utilized the MSAA-Net deep CNN that was hosted on TensorFlow. The dataset downloaded from Kaggle was used to train the model that was suggested here. It may be broken down into two categories: water bodies and masks. There are a total of 2841 photos captured by the Sentine1-2 satellite, along with 2841 associated masks. The findings indicate that our technique provides the highest possible level of segmentation performance, outperforming both the traditional and more contemporary methods. In addition, the technology that we have presented is resilient in difficult extraction circumstances, including aquatic bodies.