Identification of intracranial haemorrhage (ICH) using ResNet with data augmentation using CycleGAN and ICH segmentation using SegAN

Multimedia Tools and Applications - Intracranial Haemorrhage (ICH) occurring due to any injury to the brain is a fatal condition and its timely diagnosis is critically important. In this work, we...     

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network (MANET), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive flooding technique, floods the network with query messages, while the random walk technique operates by contacting the subsets of every node’s neighbors at each step, thereby restricting the search space. One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource. Many earlier works have mainly focused on the simulation-based analysis of flooding, and its variants under a wired network. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of P2P systems running over MANET. In this paper, we describe how P2P resource discovery protocols perform badly over MANETs. To address the limitations, we propose a new protocol named ABRW (Address Broadcast Random Walk), which is a lightweight search approach, designed considering the underlay topology aimed to better suit the unstructured architecture. We provide the mathematical model, measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques. Further, we also derive three relevant search performance metrics, i.e., mean no. of steps needed to find a resource, the probability of finding a resource, and the mean no. of message overhead. We validated the analytical expressions through simulations. The simulation results closely matched with our analytical model, justifying our findings. Our proposed search algorithm under such highly dynamic self-evolving networks performed better, as it reduced the search latency, decreased the overall message overhead, and still equally had a good success rate.     

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

Despite the seemingly exponential growth of mobile and wireless communication, this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication, Bluetooth, and Wi-Fi to achieve better network connection which in turn gives the best quality of service (QoS). Many analysts have established many handover decision systems (HDS) to enable assured continuous mobility between various radio access technologies. Unbroken mobility is one of the most significant problems considered in wireless communication networks. Each application needs a distinct QoS, so the network choice may shift appropriately. To achieve this objective and to choose the finest networks, it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires, dependent on QoS measures. Therefore, the main goal of the proposed system is to provide an enhanced vertical handover (VHO) decision making program by using a Multi-Criteria Fuzzy-Based algorithm to choose the best network. Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover. Furthermore, a double packet buffer is utilized to decrease the packet loss by 1.5% and to reduce the number of handovers up to 50% compared to the existing systems. In addition, the network setup has an optimized mobility management system to supervise the movement of the mobile nodes.     

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window-based attack detection in 6G

Regardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth-generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post-quantum cryptography, and so on. AI-powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window-based (LA-HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large-scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)-based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window-based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA-HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA-HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.     

Improved K-Means Based Q Learning Algorithm for Optimal Clustering and Node Balancing in WSN

Wireless Personal Communications - A wireless sensor network is a potential technique which is most suitable for continuous monitoring applications where the human intervention is not possible. It...     

A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed-kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.     

Spatially Controlled Transience of Graphene‐Polymer Electronics with Silicon Singulation

Transient electronics are an emerging technology for civilian and government applications that require controlled disintegration of an electronic chip into smaller components, by physical or chemical means. Here, a pillar‐on‐polymer architecture is presented for a transient system where the electronic components are partitioned on an array of silicon pillars. The pillars are mechanically tethered by a vaporizable polymer film and electrically routed with atomically thin graphene interconnects. Polymer vaporization is achieved with Joule heating of thin‐film metal heaters associated with each silicon pillar, which singulates the pillar. The pillar singulation breaks the graphene interconnects locally, without collateral damage to other on‐chip components. This process demonstrates a methodology for temporally and spatially controlled transience as any single pillar can be singulated at any time. A novel polymer‐silicon layer transfer fabrication process is used to microfabricate a 3 × 3 array of 200 µm diameter silicon pillars spaced 200 µm apart, with gold heaters and graphene interconnects, and the controlled singulation of individual pillars is demonstrated. As a demonstration of a sensor in this technology, a piezoresistive accelerometer is integrated with this platform, which uses a silicon pillar array suspended from the polymer film as a proof mass.     

CE2RV: Commissioned energy-efficient virtualization for large-scale heterogeneous wireless sensor networks

The Internet of Things (IoT) comprises sensor networks, intelligent things, devices, and humans for heterogeneous services and applications. Energy constraints in conventional wireless networks impact IoT performance resulting in service failures. For reducing the adverse impact of energy, this article introduces a commissioned energy-efficient resource virtualization (CE2RV) scheme. This proposed scheme classifies the sensor nodes as fast and slow-depleting for identifying service failures. The fast-depleting nodes are discontinued from the service replications, and the remaining energy high-node-connected resources are identified. Such resources are virtualized for thwarting the existing energy failures over various services. The node classifications are performed using a tree-learning algorithm. The classifications are performed for node replacement and service virtualization under different energy depletion rates. This is required for preventing sensor network disconnections between the users and service providers. The classification is required for overcoming multiple virtualizations between common nodes across different service providers. The proposed scheme's performance is analyzed using the metrics of service disconnections, energy utilization, energy efficiency, and service delay.     

Ultrasensitive diagnosis for an anthrax-protective antigen based on a polyvalent directed peptide polymer coupled to zinc oxide nanorods

A flexible poly-D-lysine polymer conjugated with different target-binding peptides is demonstrated with an ultralow concentration detection limit compared to those of other conventional detection systems. This polyvalent directed peptide polymer (PDPP) exhibits increased binding affinity and detects anthrax protective antigen at low levels using a well-known zinc oxide nanorod detection system.