Effective research has been aimed at increasing the distributed compute dependent Software Define Network (SDN) with high-level Intelligent - Internet of Things (I-IoT). Wireless sensor networks come with a set of resource restrictions. Still, only a few functions are often configured such as energy restraint and the concerted demands that are vital for IoT application routing performance. A major technique for solving the expansion of network scalability by applying Mobile Sink (MS). The construction of data transmission optimal path, the detection of an optimal set data-gathering points $O_{DG} $ and MS scheduled with dynamic networks for energy-efficient techniques, that the network’s lifetime in enormous complications, principally in large-scale IoT networks. The research work proposes an Research Objective: i) Develop an energy-efficient routing technique for large-scale I-IoT networks within a cloud-based SDN system. ii) Optimize network scalability, lower-level routing, and load balancing using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The prime aim of cloud-based SDN with AI is to determine: a lower level routing in the perception layer, a load-balanced Cluster Table (CT), an optimal $O_{DG} $ points, and MS optimal paths $O_{MSpath} $ . The main contribution of proposed routing is i) Energy Minimization (EM): The proposed routing minimizes energy dissemination by the Cluster Head (CH) in critical conditions (EM-CH). ii) Enhanced Energy Balance (EEB): The EC-based SDN, considering both Optimal Data-Gathering ( $O_{DG}$ ) and Mobile Sink (MS) advancements, achieves enhanced energy balance during network routing (EEB-SDN). Research results validate the proposed model stability that improves the network lifetime up to 63%, the energy usage in the network is reduced up to 78%, the high volume data loaded to the MS up to 95%, and the delay of the $O_{MSpath} $ by 69% when compared with various model.

The cardiovascular system is responsible for carrying the blood along with nutrition and oxygen throughout the body. This system consists of heart, blood, and blood vessels. The experts, or doctors called as cardiologists, analyze the sounds of heart’s (lub-dub) beat and flow of blood to diagnose Cardio Vascular Disease (CVD) using a traditional stethoscope and phonological cardiogram technique. Through the stethoscope, the cardiologist will listen to vibration produced by heart beat and heart beat sound and murmur sound are popularly known as phonocardiogram (PCG) signals, which are being recorded for medical diagnosis purposes using a stethoscope. The development of a technique for the automatic recognition of HVD’s assists the experts in recognizing the CVD effectively in the initial stage itself from PCG signals. There are many tools available to help doctors in a clinical setting for the accurate diagnose the CVD in a less time. The main aim of this proposed work is to provide an Artificial Intelligence (AI) based PCG signal analysis for the automatic and early detection of various cardiac conditions using supervised and unsupervised Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (Bi-LSTM) Machine Learning (ML) algorithm. Along with this algorithm, Generative Adversarial Networks (GAN’s) is considered because they can create fresh, high-quality, pseudo-real data that resembles their training set which has been demonstrated by using their two unique networks: Discriminator Network (DN) and the Generator Network (GN). The proposed method is tested using heart sound signals from the well-known, freely accessible PhysioNet and Kaggle datasets. The Experimental results are validated based accuracy, precision, F1-score, sensitivity, and specificity.

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer’s disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer’s prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.

Wireless sensor networks (WSNs) are imperative to a huge range of packages, along with environmental monitoring, healthcare structures, army surveillance, and smart infrastructure, however they’re faced with numerous demanding situations that impede their functionality, including confined strength sources, routing inefficiencies, security vulnerabilities, excessive latency, and the important requirement to keep Quality of Service (QoS). Conventional strategies generally goal particular troubles, like strength optimization or improving QoS, frequently failing to provide a holistic answer that effectively balances more than one crucial elements concurrently. To deal with those challenges, we advocate a novel routing framework that is both steady and power-efficient, leveraging an Improved Type-2 Fuzzy Logic System (IT2FLS) optimized by means of the Reptile Search Algorithm (RSA). This modern framework employs weighted ensemble clustering and matched ensemble choice techniques, facilitating strong cluster formation and resulting in a substantial 98% development in community lifetime, an 80% enhancement in packet delivery ratio (PDR), and a 45% reduction in average power consumption while as compared to current protocols. The IT2FLS model dynamically adapts routing selections by means of assessing key parameters, consisting of dynamic accept as true with factors, node power ranges, course delays, and energy consumption fees, whilst the RSA algorithm exceptional-tunes the bushy common sense club features to make certain most beneficial cluster head choice and reliable multi-hop communication paths. Additionally, our proposed technique complements secure routing by means of efficaciously counteracting the effects of compromised nodes, for this reason maintaining community integrity. Multiple test results show that RSA’s enhanced IT2FLS system consistently outperforms new standards. It reduces end-to-end work by 25% and significantly increases productivity. These findings suggest that this robust routing model not only guarantees efficient data transmission and performance. But it also guarantees the stability of the overall network. This research contributes significantly to the WSN field by providing a comprehensive routing solution that integrates energy conservation, security, and QoS, ultimately extending the lifespan of WSNs and boosting confidence in critical applications, paving the way for implementation scenarios that require secure, low-power, and real-time data transmission.

The liver is a very important and complex organ in our body. Efficient liver segmentation is a very important interesting and challenging task in the field of medical image processing. The abdomen consists of many organs among which the liver consists of very important parts, such as right lobule, left lobule and hepatic duct. In this research work, for computed tomography (CT) images we designed a feasible upstream approach to segment a liver region from abdomen using multi-level deep convolutional network and Fractal Residual Network (FRN). Our convolutional neural network is designed in such a way that it learns to allot different probabilities for each super pixel based on different regions of the liver by these indirectly different classes are created based on different intensities of super pixels. Further FRN is used to identify the tumor region and finally refinement of tumor segmentation is done with active contour model method. The proposed model (MDCN + FRN) is evaluated on CT images 125 patients from TCI dataset and achieved a dice similarity in an average of 0.89 in training and dice similarity in an average of 0.86 in testing. Compared to other segmentation methods, the experiment conducted shows better performance from proposed method.