The early prediction of Alzheimer’s disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art.

Vehicular Ad Hoc Networks (VANETs) are autonomous and self-configurable wireless ad hoc networks and considered as a subset of Mobile Ad Hoc Networks (MANETs). MANET is composed of self-organizing mobile nodes which communicate through a wireless link without any network infrastructure. A VANET uses vehicles as mobile nodes for creating a network within a range of 100 to 1000 meters. VANET is developed for improving road safety and for providing the latest services of intelligent transport system (ITS). The development and designing of efficient, self-organizing, and reliable VANET are a challenge because the node's mobility is highly dynamic which results in frequent network disconnections and partitioning. VANET protocols reduce the power consumption, transmission overhead, and network partitioning successfully by using multicast routing schemes. In multicasting, the messages are sent to multiple specified nodes from a single source. The novel aspect of this paper is that it categorizes all VANET multicast routing protocols into geocast and cluster-based routing. Moreover, the performance of all protocols is analyzed by comparing their routing techniques and approaches.

Vehicular Ad-Hoc Networks (VANETs) are a challenging yet active research area. It offers a wide range of applications, including Intelligent Transport System (ITS), effective road traffic monitoring, efficient traffic flow and road safety applications. During real-time data gathering for emergency scenarios, the fixed silent segments cause a problem for smooth communication. Moreover, the critical ITS operations may be delayed due to this problem. This paper proposes a Real-Time Traffic-Aware Data Gathering Protocol (TDG) where the dynamic segmentation switching is adopted to handle the communication limitations. TDG is lightweight and dynamically designed for collecting and forwarding data packets based on current and rapid evolving traffic conditions. The primary objective is to reduce network and data communication overhead to incorporate real-time data collection time constraints. TDG implements a data aggregation scheme for data analysis to fetch information based on location, speed, vehicle id and neighbour count. Moreover, a data extraction scheme is implemented to increase data retrieval and data utilization effectiveness in an intelligent way at the base station. Extensive simulation and evaluation results validate that our proposed solution outperforms existing data gathering protocols in effectiveness, efficiency, delay, communication overhead and data transmission rate.

Unmanned ground vehicles (UGVs) are becoming the foremost part of rescue teams for protecting human lives from severe disasters and reducing human casualties by informing them about the risks ahead, such as mine detection and clearance. In mine detection, a centralized system is required so that the UGVs can communicate with each other efficiently to disseminate the mine detection messages (MDMs) to incoming vehicles of military and civilians. Therefore, in this piece of research, a novel unmanned ground and aerial vehicle (UGAV)-based mine-detection-vehicle routing (MDVR) protocol has been proposed, mainly for the mine detection and clearance teams using a vehicular ad hoc network (VANET). The protocol disseminates the MDMs using UGVs and unmanned aerial vehicles (UAVs) in combination to overcome the limitations of only inter-UGV communication. The proposed protocol performs cluster-based multicast communication in real time using UAVs so that the dynamic mobility of UGVs cannot affect the performance of MDM dissemination. Hence, the proposed scheme is adaptable because any failure in message delivery can cause a high level of destruction. The proposed cluster-based scheme can adapt to any real-time scenario by introducing the level-based cluster-head election scheme (LBCHE), which works concerning its assigned priority for reducing the delay incurred in MDMs dissemination. The simulation of the proposed protocol in the network simulator (NS) shows that the overhead and delay are reduced in MDMs dissemination. At the same time, the throughput, packet delivery ratio, and stability increased compared to the other competing protocols.