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The diagnosis of brain tumours (BT) is time-consuming and heavily dependent on the radiologists' abilities. Multiple algorithms have been developed for detecting and classifying BT that are both accurate and fast. Recent years have seen an increase in the popularity of deep learning, especially when it comes to developing automated systems that can diagnose and segment BT more accurately and with less time. In this paper, a novel Brain Hexagonal Pattern Network (BHPN) has been proposed to classify the MEG and PET images into normal, benign and malignant tumours. For pre-processing, a bilateral filter is employed to remove noise artifacts from the collected MEG and PET images. To remove the outer cortical and skull region, skull stripping is used, to be implemented to raise the volume of the training datasets. The pre-processed images are segmented using the Otsu threshold algorithm to segment the BT. These segmented tumours are taken as input to the Reversing Hexagonal algorithm to generate the hexagonal feature sets with and without a segmentation mask. In order to categorize tumours into normal, benign and malignant cases, a Spiking Dilated Convolutional Neural Network (SDCNN) classifier system is implemented. The classification accuracy of the Proposed BHPN approach is 99.54%. The Proposed BHPN approach improves the overall accuracy by 1.49%, 2.52%, and 3.93% better than hybrid deep autoencoder (DAE) and Bayesian Fuzzy Clustering (BFC), Deep CNN, and Neutrosophy and Convolutional Neural Network (NS-CNN) respectively. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of several independent sensor nodes that are able to interpret, analyze, and work with data. It is generally recognized that security and limited energy are the two challenging tasks with WSNs. To address these challenges, a novel Aquila-optimized fuzzy deep belief network (AO-FDBN) model has been proposed in this paper. The suggested AO-FDBN framework consists of three stages. Initially, the Aggregator has been selected by using the Aquila optimization algorithm. Secondly, the data from the aggregator are encrypted by using the blowfish algorithm. Finally, the optimal route has been selected by using the fuzzy-deep belief network (DBN). Packet delivery ratio (PDR), transport delay, energy usage, and network lifetime are evaluated between the suggested framework and current methods. Experimental results specify that the suggested AO-FDBN approach achieves higher performance of 22.617%, 14.22%, and 15.64% than TEEFCA, HESC, and SEPC methods. This system is more effective and secure for real-time applications. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics. [ABSTRACT FROM AUTHOR]

The recent advancements in ad-hoc networks have resulted in innovations like FANETs. FANETs (Flying Ad hoc Networks) have become diversified in its applications ranging from agriculture, military, emergency services etc. The FANET environment needs to be dynamic and is populated by UAVs. The highly mobile UAVs are responsible for data transmission between nodes. If unchecked, this results in packet loss. Reliable data transmission in FANETs is possible, if effective routing protocols are in place. In this research, a Secure Waterfall Energy-Efficient Protocol-Enabled Routing (SWEEPER) has been proposed which helps conserve the node energy throughout the transmission process. The framework uses a waterfall model approach which includes group key management as an underlying process governing the security aspect of the protocol. Asymmetric key cryptography is applied to our technique, which involves two unique nodes, labeled the Computed Key (CKey) and the Dissemination Key (DKey). The two nodes will generate, verify, and distribute the secret keys. This will help other nodes concentrate on transmission alone and needn't waste time in computational activities or key handling. Security breaches and malignant nodes are also handled efficiently. The nodes along the route are selected based on a trust factor, which allows our protocol to select only genuine nodes to forward packets along the discovered path. Our work is analyzed with existing protocols of FANETS like SecRIP and MDRMA. The analysis reveals that our protocol outperforms the existing protocols in terms of minimal delay, maximum energy conservation and PDR, which contributes towards a maximized throughput. [ABSTRACT FROM AUTHOR]

This FANETs (Flying Adhoc NETworks) are supported by UAVs (Unmanned Aerial Vehicles) which carry out the task of transmitting and receiving data. Transmission and routing are complicated by the highly mobile AUVs. Node links are temporary and may result in transmission related issues. FANETs are prone to network attacks, especially black hole attacks leading to heavy data loss. This may lead to packet drops which might compromise the transmitted data. The proposed Eagle Optimized Energy Efficient Optimal Route-Finding Protocol (EOEEORFP) selects an efficient route towards the destination. Factors like trust degree, priority queue, network delay, hop count and energy consumption levels are taken into consideration in designing the routing strategy. These factors help in identifying the nodes that form the trusted nodes in the routing path. This is achieved by employing the biological behavior of eagles in finding their prey. The suggested protocol works in two stages. In the initial stage, it sets up an optimal route with selective nodes (trusted nodes). In the second stage, the network security is fortified by eradicating black hole attacks by using RREP (Route Reply) packets strategically. The EOEEORFP protocol assures the successful data delivery by employing a trusted routing path. The proposed EOEEORFP protocol is assessed against Adhoc On demand Distance Vector (AODV) and AODV-V2 protocols to measure its performance. EOEEORFP achieves very good PDR and throughput while conserving the network energy by minimizing the network delay. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing [OFDM] is an information transfer technique in which a single data flow is divided between several closely spaced narrowband subchannel frequency range rather than a single Wideband channel frequency. The information is sent to the relay node there is a delay and some data is lost in the relay node is the major issue in the existing system. To overcome these challenges, The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. In this paper, a novel Five Input Hybrid Optimization Relay Node Selection and Energy Efficient Routing (FIHORNSEER) technique has been proposed for choosing the best relay based on noises. Ant Lion Optimization (ALO) is initially utilized to select the relay node's elite position. Secondly, the Crow Search Optimization (CSO) Algorithm is used for the phenomenon of position and memory of each relay. Finally, the Memetic Algorithm (MA) was generated by integrating the Ant Lion and Crow search optimization algorithm for the best relay node selection. The proposed framework is compared with previous techniques like FRNSEER, LMMSE, and HABO-OFDM Methods in terms of performance analysis, such as average utility, Energy Consumption, and Network Life Time. The result shows that the proposed FIHORNSEER improves the energy consumption better than 22.01%, 16.4%, and 12.2% FRNSEER, LMMSE, and HABO-OFDM, respectively. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency-Division Multiplexing (OFDM) is a form of digital systems and a way of encoding digital data across multiple frequency components that is used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a serious disadvantage of OFDM. In this research, Fuzzy based Slime Mould optimization for CFO (FSM-CFO) has been proposed. The proposed FSM-CFO not only estimates the CFO with increased precision, but also allocates resources effectively, achieving maximum utilization of dynamic resources. Initially, Slime Mould Algorithm is utilized to extract the precise CFO. Additionally, the base station (BS) manages the Resource Units (RU), which could be used to distribute resources in such a manner that the user-requests are met. The resources are assigned using fuzzy rules, with the type of resource that is most appropriate for the nodes that have requested the resources being chosen. To assign resources to a certain job, fuzzy rules are devised. Once the residence time for the duration has passed, the highest-priority tasks receive the specific resources. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method. The performance of the proposed method is evaluated in terms of Resource utilization, failure rate, life span, and energy consumption. According to the simulated results, the suggested configuration can perform better compared to the existing techniques. [ABSTRACT FROM AUTHOR]

Cervical cancer is the most frequent and potent form of cancer in women. The complications caused by it can be avoided if it is detected and treated promptly. In oncology, artificial intelligence has improved the prediction accuracy in the preliminary stages of cervical cancer. In this work, a novel machine learning-based approach is introduced to predict and categorize the healthy and anomalous cervical cells. Initially, an adaptive median filter is utilized to eliminate the noise artefacts in the pap smears from the Herlev dataset. The auto-encoder (AE) is employed to extract the features and reduce the dimension of features to progress the training process. Consequently, a cascaded multilayer perceptron (c-MLP) is employed for the classification of the normal and abnormal cervical cells. The c-MLP was trained using the Bayesian Regulation algorithm to generate the best classification accuracy of 97.63%. As a result, the classification using the c-MLP is more accurate and effective for classifying healthy and malignant cervical cells than the classic ML classifiers. The proposed ML-based framework progresses the overall accuracy range by 3.61%, 4.40%, 5.07%, and 4.66% better than SVM, XGB, RF, and DT, respectively. [ABSTRACT FROM AUTHOR]

The tremendous growth of internet-connected devices can lead to network congestion, making it essential to incorporate new technologies to optimize performance. Thus, Device to Device(D2D) communication has been deemed as an emerging technology that can be used to communicate efficiently. However, establishing a secure and reliable mechanism for Device-to-Device (D2D) communication that ensures security, reliability, and availability poses a significant challenge. To tackle these issues a novel deep learning-based security-reliability-availability aware multiple D2D environment (SERAV Deep-MAD) has been proposed for Secure D-2-D Communication in a Fog environment. The proposed method utilizes a Fully Homomorphic Quantum Diffie Hellman Encryption (FHQDHE) to secure the data while transmitting. The proposed method utilizes the novel secure Quantum Diffie Hellman key exchange (QDHKE) technique for sharing the key to transform the plain data into cipher data, then applies FH operations on the encrypted data before transferring it to the Fog environment. Whenever a client requests data from the Fog, the Fog provider verifies the client's access rights. Moreover, the Attention-based Bi-GRU model is utilized to categorize whether the device is non-attack or attack, if the data is attacked then the proposed model classifies multiple attacks. The NS2 Simulator is used to verify and validate the proposed SERAV Deep-MAD model, and resiliency analysis is done to assess performance. The proposed techniques attain a higher accuracy of 98.18% which is 1.58%, 2.02%, and 2.41% better than MECC, MIMO, andAAKA-D2D respectively. [ABSTRACT FROM AUTHOR]

The 5G communication network is a promising technology that offers user-centric connectivity to customers, enabling quick, high-capacity, and latency-free access to many applications. Software Defined Networking Controller (SDNC) is an emerging technology that is used for increasing the security and maintaining the dynamic nature of mobile devices in latency and energy-assured 5G wireless communication. Different SDN topology open flow controllers are used in the literature for increasing the throughput and security of the network but they possess some drawbacks such as frequent location update (LU) overhead, high latency, and high energy consumption. This paper proposes a hexagonal software defined network (H-SDN) controller to overcome these challenges. The proposed H-SDN topology is used to preserve Intermediate Message Layer (IML) location during multimedia data transmission. A Core Switch (CS) updates an IML's location within clusters of cells, as long as it remains within the cluster. However, in the proposed method, there will be a location update on the server if the IML departs the cluster. Experiment results show that the proposed method can increase security, which in turn increases throughput, while reduces the location update costs, energy consumption, and transmission latency. [ABSTRACT FROM AUTHOR]