When enabled by the internet of health things (IoHT), brain neuroscience may conduct online analysis of brain information through multi‐variate electroencephalogram (EEG) classification, which would be a requirement for the recent surge in biofeedback technologies and medical supervision. With the ever‐increasing privacy issues and vulnerabilities of conventional methods, a universal and reliable‐based authentication framework for smart IoHT application with 5G technology (healthcare 5.0) is needed. Research teams have come to trust the EEG features because of their reliability, durability and universality. Fortunately, the testing paradigm's restricted functionality and poor classification accuracy have kept an EEG‐based identity authentication scheme from being widely seen in IoHT scenarios. However, due to unsatisfactory categories and the failure of a reliable identity authentication scheme, it remains important in research challenges. This research presents the design of an EEG identity authentication system supported via convolutional neural network classification includes cloud support storage methodology in the healthcare 5.0 environment, resulting in extremely high reliability, consistency and protection for the next generation of smart systems. The experimental results indicate that the accuracy and efficacy of the user authentication expect a higher legal probability of success and a lower unauthorized likelihood of success from a safety perspective. As compared to other frameworks, traditional EEG‐based authorization approaches test results to reveal that the proposed methodology yields the desired classification accuracy of 97.6%. The experiment performance on an authentication scenario is structured to prove that the proposed method is efficient, reliable and accurate.

In general, the biggest problem with a mobile ad-hoc network is the threat to its security. This is because the mobile ad-hoc network is dismantled after a certain period of time, which spends a lot of time calculating its stability and greatly wastes its security dimensions. Thus the security features on these temporary networks need to be strengthened as they pose the most threats. In this paper, a security algorithm designed in SID mode is proposed to fix security vulnerabilities in the wireless mobile ad-hoc network module. Its main feature is that its security definitions are defined according to the number of Service Package Identification assigned to it. The definition of numbers based on its importance is to make a list of related devices in order and, accordingly, bring those devices into the security module. Its security features have been improved so that the security modules remain active as long as the network is active.

One of the primary challenges in Wireless Sensor Networks (WSNs) is security. This research proposes an efficient fuzzy trust evaluation of cloud collaboration outlier detection (FTCO) in WSNs to ensure security in clustered WSNs. In the beginning, an interval type-2 fuzzy logic con-troller is used for trusts estimation in an open wireless medium to deal with transmission uncertainty. Then, to prevent fraudulent nodes from becoming cluster heads (CHs), an outlier detection based on density approach is employed to obtain an adaptive trust threshold. Furthermore, a fuzzy-based CHs election mechanism is proposed to strike a balance among security assurance and energy conservation, with a normal sensor node with greater residual energy or lower trust in various nodes having a higher possibility of being the CH. Experimental tests show that our fuzzy-based clustering technique can highly defend the network against assaults from hostile or the compromised nodes within the network and the network lifetime is found to be 40 % higher than other systems.

Diabetes causes damage to the retinal blood vessel networks, resulting in Diabetic Retinopathy (DR). This is a serious vision-threatening condition for most diabetics. Color fundus photographs are utilized to diagnose DR, which necessitates the employment of qualified clinicians to detect the presence of lesions. It is difficult to identify DR in an automated method. Feature extraction is quite important in terms of automated sickness detection. Convolutional Neural Network (CNN) exceeds previous handcrafted feature-based image classification algorithms in terms of picture classification efficiency in the current environment. In order to improve classification accuracy, this work presents the CNN structure for extracting attributes from retinal fundus images. The output properties of CNN are given as input to different machine learning classifiers in this recommended strategy. This approach is evaluating using pictures from the EYEPACS datasets using Decision stump, J48 and Random Forest classifiers. To determine the effectiveness of a classifier, its accuracy, false positive rate (FPR), True positive Rate (TPR), precision, recall, F-measure, and Kappa-score are illustrated. The recommended feature extraction strategy paired with the Random forest classifier outperforms all other classifiers on the EYEPACS datasets, with average accuracy and Kappa-score (k-score) of 99% and 0.98 respectively.

Deep learning models are capable of performing sophisticated calculations, but they''''re not suitable for mobile and handheld devices due to their vast size and demanding computational requirements. Using an automated process, we intend to identify the diseases of plants so that we can build a process which begins with pre-processing, separates diseased leaf area, calculates features based on the Gray-Level Co-occurrence Matrix (GLCM), chooses and classifies features, and ends with decision making. Through threshold segmentation, we were able to isolate the diseased leaf areas in the maize plants, and then use this information to create fuzzy decision rules for the assignment of images of Common Rust to its severity class. These results were obtained with six colour and texture features. Plant disease clustering is performed with the Fuzzy Algorithm. The measurements show higher performance to the conventional methodologies and are ranked highest in terms of feature extraction method. This suggests that leaf-based plant disease diagnosis is the most appropriate method. These capabilities can be considered by the addition of new disease classifications or specific crop or disease classifications.

In recent decades, there exists a wide increase in traffic of clouds due to the enormous increase of media content. The popularity of cloud is gaining its attention due to its ease of use and flexible model but suffers from poor resource2 management and its minimal extendibility of service portfolio. However, with recent advancement, the services are effectively managed, and its discovery is made further possible. To handle larger amounts of multimedia contents in a standalone cloud, deployment of wide operable systems is yet required that handles the data effectively with increasing demands of the user. A resource allocation framework is designed in this paper that uses a gray wolf optimization (GWO) architecture to effectively learn the operation of resource allocation in an optimal manner. For optimal service provisioning and scalability, the cloud at times communicates with each other based on the resource allocated by the deep neural network, and then the resources are shared. Such a scenario forms the multi-cloud computing, and the resource management using the deep neural network ensures trivial solutions on poor scalability. The deep neural network acts as a model for controlling the routing capabilities based on the input data rate and the storage space available in the multi-clouds. The deep neural network operates in such a way that it reduces the delay in processing and storage of data to cloud that ensures flexible operations across the cloud. The entire operation is divided into two modules: the first module operates on data processing and routing operations, and the second module acts as a control plane using the deep neural network that ensures optimal allocation of resources based on the data obtained and processed in the first module. These two models ensure better delivery of data to the cloud with proper allocation and storage of resources in the multi-cloud environment. The simulation is conducted using Java (netbeans) platform, and it is evaluated further using CloudSim toolkit. The results are experimented on various performance metrics that includes time delay and cost of resource allocation on multi-cloud.

1,2UG Scholar, Hindusthan College of Engineering & Technology, Coimbatore 3Professor, Dept. of Electronics & Communication Engineering, Hindusthan College of Engineering & Technology, Coimbatore, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Hydroponics is a technique in which we grow the plant without using the soil. In This technique we ensure that plant gets all nutrients from the water solution. There are many types of hydroponics system. The Ebb & Flow is one of the hydroponics technique types. In this technique that grows the plant by supplying the nutrient direct to the root of the plant until the plants harvested. By using this technique, the plant roots will be always deeped into the water contains nutrient and oxygen. However, this technique manually controls the purity of water, which can effect to growing of plant. In this the purity level in water solution will be automatically maintained by microcontroller and measured by turbidity sensor. Lastly, this research also focuses on the ability of the system can adjust the purity in water solution for Ebb & Flow system. The water solution from the Ebb & Flow system container is transferred to the main tank to measure the purity level by turbidity sensor and make changes if needed and then transfer back to the system container to continue growing the plant. There are six stages in methodology for this project, which are details of study, hardware identification, software identification, hardware and software interfacing, analysis and troubleshooting, data and result collection.

The unused or under-utilized TV bands are opportunistically utilized by Cognitive Radio enabled IEEE 802.22 Wireless Regional Area Networks (WRAN). However due to the nature of cognitive radio networks and lack of proactive security protocols, these networks are vulnerable to various Denial of Service (DoS) threats. In this paper the target band chosen for attack is a specific band called as Most Active Band (MAB) which has most signal activities among the available bands. Co-ordination among the malicious nodes in MAB is analyzed to produce maximum net outcome. Simulation results are provided to demonstrate the effectiveness of the proposed co-ordinated MAB attack.

The unused or under-utilized TV bands are opportuni stically utilized by Cognitive Radio enabled IEEE 802.22 Wireless Regional Area Networks(WRAN). However due to the nature of cognitive radio networks and lack of proactive security protocols, the IEEE 802.22 networks are vulnerable to various Denial of Service (DoS) threats. In this paper the target ban d for DoS attack is a specific band called as Most User Band which has maximum number of users among the available sub bands in the CR network. We propose a countermeasure strategy (Time concealment strategy), to counter the MUB attack. Simulation results a re provided to demonstrate the effectiveness of the pr oposed MUB attack and TCS with attack time control for further survival improvement of secondary nodes .

The demand for fast transfer of large volumes of data, and the deployment of the network infrastructures is ever increasing. However, the dominant transport protocol of today, TCP, does not meet this demand because it favors reliability over timeliness and fails to fully utilize the network capacity due to limitations of its conservative congestion control algorithm. The slow response of TCP in fast long distance networks leaves sizeable unused bandwidth in such networks. A large variety of TCP variants have been proposed to improve the connection's throughput by adopting more aggressive congestion control algorithms. Some of the flavors of TCP congestion control are loss-based, high-speed TCP congestion control algorithms that uses packet losses as an indication of congestion; delay-based TCP congestion control that emphasizes packet delay rather than packet loss as a signal to determine the rate at which to send packets. Following three TCP variants, namely Tahoe, New Reno and Vegas were compared using throughput, round-trip time (RTT) and packet loss ratio. While all the TCP variants achieve similar throughput, they do so in different ways, with different impacts on the network performance. The adverse effects of TCP window auto-tuning is identified in this environment and demonstrate that on the downlink, congestion losses dominate wireless transmission error. Several issues were revealing for this WiMAX-based networks, including limited bandwidth for TCP, high RTT and jitter, and unfairness during remote login, VoIP, and video streaming.