The satellite communication is embellished constantly by providing information, ensuring security, and enables the communication among huge at a particular time efficiently. The satellite navigation helps in determining the people's location. Global development, natural disasters, change in climatic conditions, agriculture crop growth, etc., are monitored using satellite observation. Hence, the satellite includes detailed information data, and it must be protected confidentially. The field of the satellite is enhanced at an astonishing pace. Satellite data play an important role in this modern world; hence, the onboard-satellite data must secure through the proper selection of error detection and estimation schema. Lightweight deep learning algorithm based on Extended Kalman Filter (KFK) is proposed to detect and estimate onboard pointing error such as an error in attitude and orbit. The Extended Kalman Filter (EKF) is widely used in the satellite system. EKF is utilized in this proposed model to detect the onboard pointing error such as attitude and orbit determination. An autonomous estimation of orbit position is possible through space-borne gravity. The information obtained through the observation of satellite data is compared with the accurate gravity model in detecting the error. The utilization of EKF reduces the dependence of the ground tracking system in satellite determination. The orbital altitude and orbital position are the most important challenges faced in the satellite determination system. The satellite model using the Extended Kalman Filter is an optimum method in estimating the orbital parameters. The errors in the linearization process are detected, and this can be overcome through the proper selection of linear expansion point with the EKF algorithmic model with the Jacobian matrix calculation. The results show that the EKF implementation helps in attaining better accuracy than other methodologies. Its contribution is enormous to many space missions, autonomous rendezvous and docking for manned and unmanned missions (e.g., ISS operations and beyond, in-orbit servicing, and in-orbit refueling), routine satellite OD operations, orbital debris removal systems, Space Situational Awareness (SSA) operations, and others.

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Onboard Pointing Error Detection and Estimation of Observation Satellite Data Using Extended Kalman Filter

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

A brain tumor is a disorder caused by the growth of abnormal brain cells. The survival rate of a patient affected with a tumor is difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role in determining the tumor site; however, manual detection is a time-consuming and challenging procedure that can cause some errors in results. The adoption of computer-assisted approaches is essential to help in overcoming these constraints. With the advancement of artificial intelligence, deep learning (DL) models are being used in medical imaging to diagnose brain tumors using MR images. In this study, a deep convolutional neural network (CNN) EfficientNet-B0 base model is fine-tuned with our proposed layers to efficiently classify and detect brain tumor images. The image enhancement techniques are used by applying various filters to enhance the quality of the images. Data augmentation methods are applied to increase the data samples for better training of our proposed model. The results show that the proposed fine-tuned state-of-the-art EfficientNet-B0 outperforms other CNN models by achieving the highest classification accuracy, precision, recall, and area under curve values surpassing other state-of-the-art models, with an overall accuracy of 98.87% in terms of classification and detection. Other DL algorithms such as VGG16, InceptionV3, Xception, ResNet50, and InceptionResNetV2 are used for comparative analysis. 

/pdf/a-robust-approach-for-brain-tumor-detection-in-magnetic-dk1ahenw.pdf

A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet

Speech emotion recognition is the crucial stream in emotional computing and also create few issues owing to its complication in processing. The efficiency of the acoustic methods and their speech features are improved using various existing methods. Yet, the conventional acoustic methods are not effective in handling speech emotion recognition because of their drawbacks. The main intend of this research is to implement a new speech emotion recognition using the hybrid deep learning model. Initially, few speech emotion recognition dataset is gathered from the public sources and is put forwarded for pre-processing using artifacts removal and filtering techniques. Then, the feature extraction of the speech signals is performed by the Mel-Frequency Cepstral Coefficients (MFCC), mel-scale spectrogram, tonal power, and spectral flux. In the aim of decreasing the feature size for boosting up the learning performance, for selecting the optimal feature is adopted by the Deer Hunting with Adaptive Search (DH-AS) algorithm. These optimal features are used for the emotion classification by the Hybrid Deep Learning (HDL) with “Deep Neural Network (DNN) and Recurrent Neural Network (RNN)”. These two networks are enhanced by the developed DH-AS, thus could reach high classification accuracy while classifying the emotions like “happy, sad, anger, fear, calm etc”. The performance of the suggested DH-AS-HDL correspondingly improves 3.15%, 5.37%, 4.25% and 4.81% better accuracy than the PSO-HDL, GWO-HDL, WOA-HDL and DHOA-HDL, when the learning rate as 85. The achieved results prove that the developed model obtains superior performance by evaluating its performance through various performance metrics. 

Hybrid deep learning with optimal feature selection for speech emotion recognition using improved meta-heuristic algorithm

An automated brain tumor classification in MR images using an enhanced convolutional neural network

Automatic classification of brain tumor plays a vital role to speed up the treatment procedure, plan and boost the survival rate of patients. Nowadays, Magnetic Resonance Imaging (MRI) is employed for determining brain tumor. However, manual identification of brain tumor is purely based on the sensitivity and experience of medical professionals. Thus, more research works towards brain tumor classification have been implemented for minimizing the human factor. Different imaging approaches are employed for detecting brain tumors. Though, MRI is mainly employed owing to the better quality of images due to the non ionizing radiation of images. One of the major categories of machine learning is called deep learning, which shows an outstanding performance, mainly on solving the segmentation and classification issues. The aim of this paper to introduce a new brain tumor classification model based on the intelligent segmentation and classification approaches. The main phases of the proposed model are (a) Data collection, (b) Pre-processing, (c) Tumor segmentation, and (d) Tumor Classification. Initially, the datasets related to the brain tumor are gathered from several benchmark sources and subjected to the pre-processing step. Here, it is performed by the median filtering and contrast enhancement techniques. The first contribution of this paper is the development of an enhanced segmentation approach termed as Adaptive Fuzzy Deformable Fusion (AFDF)-based Segmentation, which merges the two concepts of Fuzzy C-Means Clustering (FCM) and snake deformable approach. Here, the significant parameters of the AFDF are optimized by the improved Deer Hunting Optimization Algorithm (DHOA) termed Adaptive Coefficient Vector-based DHOA (ACV-DHOA). The classification of images is performed by the Optimized Convolutional Neural Network with Ensemble Classification (OCNN-EC) after segmenting the tumor. In the proposed deep learning classification, the number of convolutional layers and hidden neurons of CNN is optimized by the ACV-DHOA, and the fully connected layer is replaced by the ensemble classifier with Deep Neural Network (DNN), autoencoder, and Support Vector Machine (SVM). The classifier which is getting high rank is considered as the optimal one. The experimentation results are performed on the standard database that shows the high classification accuracy of the developed model by evaluating with other conventional methods.

Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis

Spectrum trading and sharing in unmanned aerial vehicles based on distributed blockchain consortium system

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

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Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The widespread use of electric vehicles (EVs) is viewed as a turning point for lower emissions of co2 and much more advanced driver assistance systems. It is time-consuming to predict the battery capacity of the electric vehicle using the endurance and dependability of battery systems. Because battery deterioration is typically non-linear, predicting the capacity of the battery of charge estimation with significantly less deterioration is incredibly time-consuming. As a result of this complexity, real-time control of battery storage has proven difficult. Nevertheless, using the latest advancements in battery deterioration comprehension, simulation techniques, and testing, there is a potential to combine this expertise with new machine learning (ML) approaches to find a potential solution for this complexity. In this study, the battery life is predicted using three different regression models Linear Regression, Ridge Regression, and Ensemble Regression. The model's error analysis is conducted to improve the battery's performance characteristics. Finally, the three ML algorithms are compared using performance parameters. Out of the three regression models, the Ensemble regression model is found to be the best model which has an accuracy of 94.8%.

Mantripragada Yaswanth Bhanu Murthy

Papers

Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis

Spectrum trading and sharing in unmanned aerial vehicles based on distributed blockchain consortium system

Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

Battery life time prediction of electric vehicle using artificial intelligence

Video Streaming in Ultra High Definition (4K and 8K) on a Portable Device Employing a Versatile Video Coding Standard