Brain Tumor Detection using MRI Image Processing Techniques Ambaji. S. Jadhav, Rashmi Pawar, Sunilkumar. M. Hattaraki, Shwetha Bagali, Rajeshwari S. Patil, et al. 2024 International Conference on Innovation and Novelty in Engineering and Technology Innova 2024 Proceedings, 2024 Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. Brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. Computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image pre-processing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MSD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection.
Wireless Patient Health Monitoring System using Internet of Things and Mobile device Jayalaxmi S. Gonal, Malini M Patil, Ambaji S Jadhav, Rashmi V Pawar Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy Icais 2023, 2023 In this current decade, the healthcare monitoring systems are gaining more importance among researchers. The major objective is to develop a robust patient monitoring system so that the health experts can track the patients’ health status, admitted in the hospitals or patients discharged but needs follow up of the doctors. This research work presents a wireless healthcare monitoring system using mobile device which delivers online data in real time about a patients’ physiological status. This proposed project is developed to monitor and quantify patients’ crucial physiological data precisely. Also, the proposed system transmits a message to alarm the serious health condition of the patient via text or email messages. Further these provide necessary medical guidelines to the healthcare professionals. The important constituents of the proposed system are data acquisition unit, sensors, Arduino (microcontroller), and software. The proposed system monitors, displays, and stores the patient’s ECG data, heart beat rate, muscles, temperature, blood glucose level and blood pressure. The results of the developed system illustrate that the physiological data of the patients are measured with high accuracy.
Diagnosis of Mammographic Images for Breast Cancer Detection using FF-CSO Algorithm Rashmi V Pawar, Santosh Saraf, Umesh Dixit, Ambaji S Jadhav Proceedings of the Accthpa 2023 Conference on Advanced Computing and Communication Technologies for High Performance Applications, 2023 Many diseases in the human being are can be detected through different imaging technology. The mammography is one of the imaging technique through which breast cancer cells can be identified. The mammographic breast image is preprocessed for eradicating the pectoral muscle in the breast cancer identification that contains a mammogram for encircling the process of detection. The cancer tissues having higher pixel intensities can be detected easier than the remaining breast region. It is difficult to classify the breast tumor tissues into malignant and benign ones. The mammographic breast image is generally pre-processed to eliminate pectoral muscle for the optimal diagnosis. Further, the active contour based segmentation is done to separate out the cancer region from entire mammographic image acquired by special technique. Segmenting accurate region of cancer will help in classification so segmentation stage has its own importance. Two different optimization algorithms are merged to improve optimization accuracy. The algorithms considered are Firefly and chicken swarm optimization (FF-CSO) is used optimize feature set and optimize error function of DBN. Once segmentation is performed on the image, the next process is to extract features from that segmented region to collect useful information for distinguishing the cases. In this we extracted LBP features from the segmented images. The deep learning architectures is for classification.
Design and Analysis of PMDC Motor for Electric Vehicles Vishwanath Chalawadi, Sanjeeth P. Amminabhavi, Ambaji S. Jadhav, Rashmi Pawar, Minaxi S. Kanamadi, et al. Proceedings of the Accthpa 2023 Conference on Advanced Computing and Communication Technologies for High Performance Applications, 2023 Increase in the number of vehicles around world is leading to increase in the consumption of fuel and rising in the cost of fuel. The world is taking step towards the methods which are alternate source for fuel. Most important is battery based electric vehicles and also cell based electric vehicles (EV). In this work we discussed about the design of electric vehicle, vehicle fabrication condition, force calculation for the electric vehicle, selection of motor for the EV application, designing the motor to get the rating of the motor. The methodology is simulated in matlab and later it is designed in hardware model. The design values provide good results as compared state of art.
Deep Belief Network based Diabetic Maculopathy Detection and Classification using Modified Chicken Swarm Algorithm Shweta Reddy, Shridevi Soma, Ambaji Jadhav, Rashmi Pawar, Govind Madabhavi, et al. 2023 International Conference on Computational Intelligence Communication Technology and Networking Cictn 2023, 2023 One of the most frequent causes of visual impairment in people with diabetes mellitus is diabetic maculopathy (DM). The macula is the important a part of the retina accountable for sharp vision. Diabetic maculopathy is a type of diabetic eye disease that affects the macula. The most prevalent and typical sign of diabetic maculopathy is diabetic macular edema(DME). In addition, people with diabetics one of the common cause for DME is visual impairment. Therefore, it is of the utmost important to detect DME region early and accurately. On the other hand, Ophthalmologists widely use optical coherence tomography (OCT) as an imaging tool to identify retinal diseases. In this paper, Chicken Swarm Optimization driven Deep Belief Network is developed for DME classification. In this method, OCT images are taken into account for the effective classification of the DME process. In this classification process, the layer segmentation stage involves segmenting 12 layers and 13 borders. Moreover, the Deep Belief Network classifier is applied in order to classify the images of OCT as normal one or DME affected region. The GAN classifier is trained by Chicken swarm algorithm such that the efficiency of classification is enhanced. This developed approach outperforms with other existing DME approach with respect to accuracy, specificity and sensitivity of 94.24%, 94% and 94% respectively.
Fuzzy Logic Control based Improved Congestion Management for Phase Shifting Transformer Ambaji S Jadhav, Rashmi V Pawar, Shweta Reddy, Sanaparveen A Devadi, Rajeshwari S Patil, et al. 2023 Global Conference on Information Technologies and Communications Gcitc 2023, 2023 Power generation and distribution has significant impact on availabity of power. In order to regulate a phase-shifting transformer, fuzzy logic is best suited application in different conditions. In several large nations, phase shifting transformers have already been built as FACTS devices. It is used to enhance power transfer, lower transmission system costs, manage congestion, increase stability, and many other things. In this work, MATLAB software is used to analyse congestion management. As the demand on power transformers rises, some transformers experience power congestion. The phase shifting transformer can handle this strain, and the optimal management of phase shifting transformers is achieved through fuzzy logic control. Our experimental results show enhancement in the results over the state of arts.
Optimization of Power System Transients in a Multi-Machine System using Unified Power Flow Controller Vinoda Patil, Ambaji S. Jadhav, C Nandakumara, Rashmi V. Pawar, Vinuta Koluragi, et al. Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems Icears 2023, 2023 For better utilization, the current system is now equipped with Flexible AC Transmission System (FACTS) devices. The Unified Power Flow Controller (UPFC), a multi-machine system coupled FACTS device, is discussed in this work along with its modelling and simulation. The effectiveness of these controllers is evaluated and the results of the minimization of the settling time of power oscillations and transient peaks of line power, bus voltages, and rotor angle between two machines, respectively, for single line to ground and three-phase fault. A thorough analysis shows that a three-phase failure is more serious than a single line to ground fault. The findings also suggest that the proposed controller improved the stability of a particular system by reducing oscillations in the power system and stabilizing the test system. In this article, a PI controller approach for UPFC is used to control power flow and voltage at matching buses. The control strategy is evaluated for the IEEE-9 bus power system network using Matlab/Simulink.
Segmentation and Classification of Retina Images using SVD Features Ambaji S. Jadhav, Rashmi V. Pawar, Pushpa B. Patil 2021 5th International Conference on Electrical Electronics Communication Computer Technologies and Optimization Techniques Iceeccot 2021 Proceedings, 2021
