Problems with uncontrolled cell growth in the human body are a major cause of cancer, like brain cancer, which occurs in the brain or the nervous system. Tumours are categorised as benign or malignant and are defined by cellular activity and structure. Cancerous tumours are invasive and might require surgical removal, so early and accurate diagnosis is crucial. A radiologist-based diagnosis manually is time-consuming and error-prone, especially in denseness regions. This work aims to solve this problem through the design and application of computer-aided design (CAD) approaches for tumour detection and classification of MRI data in the brain. The method starts from the pre-processing of images by Anisotropic Diffusion with Median Filtering (ADWMF) for noise removal and for the enhancement of the tumour edge. For the segmentation, we employ the IHMFKC algorithm. Feature extraction is performed based on GLCM and FOS. In total, five classifiers (i.e., YOLOv3, Faster R-CNN, ResNet-50, PNNsf3, KNN) are realised via IHMFKC. As a result, IHMFKC embedded with PNN and YOLOv3 obtained classification accuracies of 96.1% and 95.71%, respectively. The experimental results confirm that the proposed method can achieve high performance, accuracy, and reliable results, and has the potential capacity to assist doctors in distinguishing between benign and malignant tumours, helping them make the best decision to save victims of brain cancer.
A. Dudhe, P. Burade· International journal of com...· 0 citations
The increasing electricity demand associated with agricultural irrigation has created a need for energy-efficient and economically viable renewable energy systems. This paper presents a techno-economic optimization framework for a solar-powered agricultural microgrid intended to supply irrigation pump loads. The proposed system integrates photovoltaic generation, battery storage, inverter-based conversion, and grid supply. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bi-Level Optimization, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to investigate alternative system configurations. The optimization considers annual operating cost, payback period, grid dependency, and operational feasibility. The obtained results show that PSO provides the minimum annual cost of Rs. 26,091.6 and the shortest payback period of 4.2 years, whereas NSGA-II achieves the lowest grid dependency of 12%. To address the practical problem of selecting an appropriate configuration from competing optimization objectives, an AI-assisted multi-criteria decision-making layer is additionally introduced. The proposed decision layer evaluates annual cost, payback period, grid dependency, and reliability according to different agricultural user priorities. The resulting two-stage framework separates optimization-based solution generation from intelligent configuration selection and provides a flexible basis for future development of adaptive and AI-enabled agricultural energy management systems.
Prasad Ramesh Phad, J. Helonde, Prakash G. Burade· International journal of com...· 0 citations
The proposed approach uses a Quantum Neural Network for machine learning for machine learning in an intelligent Cardiovascular Disease (CVD) prediction system that has the highest sensitivity and specificity in the current literature, matching exact expert opinions.
Hutashani B. Rayate, Mangesh D. Nikose, Prakash G. Burade· International journal of com...· 0 citations
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