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 development of the fifth generation (5G) and sixth generation (6G) wireless networks has gained wide spread importance in all aspects of life through the network due to their significantly higher speeds, extraordinarily low latency, and ubiquitous availability. Owing to the importance of their users, components, and services to our everyday lives, the network must secure all of these. With such a wide range of devices and service types being present in the 5G ecosystem, security issues are now much more prevalent. Security solutions, are not implemented, must already be envisioned in order to deal with a range of attacks on numerous services, cutting-edge technology, and more user information available over the network. This research proposes the dual integrated neural network (DINN) for secure data transmission in wireless networks. DINN comprises two neural networks based on sparse and dense dimensions. DINN is designed for any presence of deep learning-based attack in a physical security layer. DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. Performance analysis suggests that DINN holds a higher level of security against the above attacks.

Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.

Cloud computing is expanding gradually as the number of educational applications is rapidly increasing. To get Educational cloud services, internet connectivity is predominantly important and Cloud Environment uses one of the basic technology to manage the Physical servers effectively ie; Virtualization Technology. In Cloud Computing, the data centers host numerous Virtual Machines (VMs) on top of the Servers. Due to the rapid growth of Educational platforms, the workload of the VM is computationally getting increased. In the Cloud Educational platforms, to execute the jobs IT resources are provisioned over the network. Since the data generated from the client-side is dynamic in nature, it is difficult to allocate the computational resources efficiently. So to enhance the energy efficiency and to provide the resources in an optimized way, a VM Scheduling mechanism with Hybrid Genetic Algorithm-Modified Particle Swarm Optimization (GA-MPSO) is proposed in this work to achieve QoS parameters like reduced Energy consumption, SLA violation, and cost reduction over the heterogeneous environments. The Hybrid G-MPSO develops the optimal range and improves the best range of scheduling the Virtual resources to VMs from Physical Machines (PMs). The proposed approach, when compared to other VM scheduling algorithms, it intensifies the energy consumption to 105KWH, SLA violation rate of 0.08%, reduces the migrations count to 2122, and consumes the overall cost of 2567.68$. The different scheduling methods for VMs are evaluated against the results, which show that the Hybrid GA-MPSO method is far better than the existing algorithms.

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platform offers personalized crop recommendations and intuitive crop planning tools, bridging the gap between traditional methods and modern technology. Multilingual support ensures inclusivity, empowering farmers with resources to enhance productivity and sustainability. Future research could focus on improving precision agriculture, climate resilience, market intelligence, and socio-economic assessments to further advance the agricultural sector.

Cloud computing has become the most challenging research field in the current information technology scenario. In this, a set of user tasks are scheduled and allocated to numerous kinds of heterogeneous virtual machines (VMs) in cloud data centers (CDCs), and these VMs are hosted by diverse types of heterogeneous physical machines (PMs). It extends several features, encompassing elasticity, safety, sustainability, and even adequate maintenance compared to traditional data centers. There are numerous techniques available for VM scheduling and allocation. However, it still requires the existence of new mechanisms that can reduce the execution time (ET) of the tasks, improve the optimization of energy usage and resource utilization (RU), and reduce time consumption. Along with optimization, VM scheduling (VMS) and VM allocation (VMA) are two-level issues that need to be considered with essential policies to govern these mechanisms. Hence, for executing optimal VMS and VMA in the data center, new optimization methodologies, such as enhanced shark smell optimization algorithm (ESSOA) at the first level and Brownian movement-centered gravitation search algorithm (BMGSA) at the second level, are proposed in this work to define the policies. Firstly, the user requests for VMs are reserved on the most appropriate PM by the proposed ESSOA, which has the lowest execution cost within deadline limits, and the proposed BMGSA decides the allocation of the chosen VM on the most appropriate PM within the resource limitations at the second level. To demonstrate the proposed algorithm’s efficiency, the simulations are carried out using the Java language-based CloudSim simulator, and the proposed mechanism outcomes acquired are compared with the existing approaches. The simulation results show that the suggested algorithm is efficient in terms of the execution cost, degree of imbalance (DOI), make span (MS), and resource utilization (RU).