ABSTRACT In this study, a novel technique termed Quadratic Discriminant Feature Selected Broken Stick Regressive Deep Convolution Neural Learning Classification (QDFSBSRDCNLC) Technique is proposed for disease classification and hence yields prediction of turmeric crop. Initially, we gathered the images of turmeric crops with and without diseases. The images are collected from the turmeric research field at Bhavanisagar. Quadratic Discriminant Analysis (QDA) is utilized to select relevant features from a dataset, reducing dimensionality. In this paper, four models, named FCN8, PSP Net, MobileNetV3 (small), and Deep Lab V3 are chosen for semantic segmentation of disease in turmeric crops. Turmeric crop production predicts is an important part of modern agriculture, allowing farmers to make sensible choices and optimize resources. We can predict turmeric crop yields accurately by using modern data analysis approaches. Predictive models take into consideration variables such as weather, soil quality, and farming techniques. The experimental results demonstrated that MobileNetV3 (small) performed better than other established ones with the accuracy of 97.99%, IoU of 96.82%, and Coefficient of 97.80% for 50 epochs. The proposed QDFSBSRDCNLC Technique effectively classifies diseases and predicts the yield of turmeric crops, with MobileNetV3 (small) showing superior performance among the tested models.

ABSTRACT Initially, a class of Caputo fractional-order bidirectional associative memory neural networks in two variables is developed, building upon the groundwork laid by delayed Caputo fractional system in one variable. Next, the Razumikhin-type uniform stability conditions, originally formulated for single-variable systems, are successfully extended to accommodate the complexities of delayed Caputo fractional systems in two variables. Leveraging this extension and employing a suitable Lyapunov function, the delay-dependent uniform stability criteria for the addressed fractional-order bidirectional associative memory neural networks are expressed in terms of linear matrix inequalities. Finally, the effectiveness and practicality of the theoretical findings are demonstrated through the application of two numerical examples, affirming the viability of the proposed approach.

ABSTRACT To improve the calculation accuracy of the Monte Carlo (MC) method and reduce the calculation time. Firstly, CNN and LSTM deep learning networks are introduced for designing nonlinear dynamic systems simulating dam stress. Then, spatial feature mining and sequence information extraction of nonlinear data of dam stress are carried out respectively, and a combined prediction model of dam stress depth (DS-FEM-CNN-LSTM) is proposed. Secondly, to solve the problem of a long time and heavy workload for the MC method to calculate a single sample point, the DOE test method is used to design the sample points. The weight factor and the distance to the failure surface are used as screening criteria. The reliability calculation method of the gravity dam (DS-FEM-CNN-LSTM-MC) is established. Finally, numerical results show that the proposed DS-FEM-CNN-LSTM-MC method performs better than the existing methods in terms of computational time consumption and accuracy.

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

ABSTRACT The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively.

ABSTRACT The poor connectivity among mobile nodes introduces uncertainty in packet loss as the path link is not measured in this network. The focus is placed on communication cost to achieve valid packet transmission. Because the high-distance path selected for packet transmission incurs higher communication costs, it increases energy consumption and packet loss rate. So, the proposed dispersed path selection for communication (DPAC) method is constructed to obtain the best minimum distance routing path. This path operates with the help of queue variation is handled the data packets maintenance, and the time slot exceeds its limit. Packets are kept waiting to increase the packet broadcasting efficiency. A multipath jamming detection algorithm is constructed to provide a link-based path packet overload detection scheme to identify the packet overload. It also separates the path based on its characteristics to control overload. It reduces energy consumption and packet loss rate.

ABSTRACT Cloud services are one of the most quickly developing technologies. Furthermore, load balancing is recognized as a fundamental challenge for achieving energy efficiency. The primary function of load balancing is to deliver optimal services by releasing the load over multiple resources. Fault tolerance is being used to improve the reliability and accessibility of the network. In this paper, a hybrid Deep Learning-based load balancing algorithm is developed. Initially, tasks are allocated to all VMs in a round-robin method. Furthermore, the Deep Embedding Cluster (DEC) utilizes the Central Processing Unit (CPU), bandwidth, memory, processing elements, and frequency scaling factors while determining if a VM is overloaded or underloaded. The task performed on the overloaded VM is valued and the tasks accomplished on the overloaded VM are assigned to the underloaded VM for cloud load balancing. In addition, the Deep Q Recurrent Neural Network (DQRNN) is proposed to balance the load based on numerous factors such as supply, demand, capacity, load, resource utilization, and fault tolerance. Furthermore, the effectiveness of this model is assessed by load, capacity, resource consumption, and success rate, with ideal values of 0.147, 0.726, 0.527, and 0.895 are achieved.

ABSTRACT The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour. So to solve these issues, a Weighted Adaptive Random Ensemble Support Vector-based Partial Reinforcement Search (WARES-PRS) algorithm is proposed that detected bone lesions accurately and also predicted the severity level stage efficiently. Further, the detection is performed with varied stages to diminish the presence of noise and undertaken effective classification. The performance is validated with CNUH dataset that enhanced image pre-processing tasks. Despite the proposed method uncover the mutual relationships between each pixel’s local texture and the overall image’s global context. The detection and classification efficiency is validated with various measures and the experimental results revealed that the detection accuracy is enhanced for the proposed approach by 98.5%. The outcomes of our study have exhibited a substantial contribution to assisting physicians in the detection of knee bone tumours.