Distributed Biomedical Scheme for Controlled Recovery of Medical Encrypted Images

As a result of their natural capacity to recover harmonic current and reactive power from alternating current sources, power electronic devices utilized in conjunction with nonlinear loads have the potential to generate significant harmonic problems within the power system when employed in this way. When this occurs, voltage instability occurs, which must be avoided in order to maintain the consistency and dependability of the power system’s power flow. With this approach, the series controller has been replaced by a multilevel modular controller in order to improve power handling capability and achieve higher modular levels with minimal distortions. The shunt compensator is the most effective way to achieve an extremely protected energy system as well as righteous steadiness in electric potential difference under a variety of load constraints. The DQ thesis is employed in this proposed converter to separate the harmonic components by establishing reference frame current, which is accomplished by machine learning techniques. As part of the constant mode operation, the PI controller contributes to maintaining the direct current-potential difference, which is given to the PWM generator. Optimization of the values of K p and K i is accomplished by the use of particle swarm optimization (PSO). The construction of this power system simulation model has been made feasible by the use of time-fluctuating characteristics modeling and the MATLAB programming environment. The new (unified power flow controller) UPFC research that has been made available is persuasive in its capacity to reduce distortions and watt-less power components while simultaneously enhancing efficiency and reducing costs.

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization and Modular Multilevel Converter Communication in Electrical Applications with Machine Learning Algorithm

Kamal

Sayeed²,

Ahanger

et al. 2022

“…The purpose is to draw attention to particular aspects of a picture or to draw attention to certain characteristics of interest (Hames et al [ 30 ]). If the brightness of bone or brain tissue in the input picture is reduced or increased, this may help to enhance the quality of the distorted image [ 31 ]. The dualistic subimage histogram equalisation approach is used in this improvement technique.…”

Section: Background Analysismentioning

confidence: 99%

Wearable Sensors with Internet of Things (IoT) and Vocabulary-Based Acoustic Signal Processing for Monitoring Children’s Health

Nagwanshi

Noonia

Tiwari³

et al. 2022

The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities which is distinct from previous research. Various brain wave patterns related to common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. As a consequence of these activities, we accumulate numerous sorts of emotion signals in our brains, including the Delta, Theta, and Alpha bands. These bands will provide different types of emotion signals in our brain as a result of these activities. As a consequence of the nonstationary nature of EEG recordings, time-frequency-domain techniques, on the other hand, are more likely to provide good findings. The ability to identify different neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker; this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. This paper presents the first time that a frequency analysis of EEG dynamics has been undertaken. An augmenting decomposition consisting of the “Versatile Inspiring Wavelet Transform” and the “Adaptive Wavelet Transform” is used in conjunction with the EEG rhythms that were gathered to provide adequate temporal and spectral resolutions. Children’s wearable sensors are being used to collect data from a number of sources, including the Internet. The signal is conveyed over the Internet of Things (IoT). Specifically, the suggested approach is assessed on two EEG datasets, one of which was obtained in a noisy (i.e., nonshielded) environment and the other was recorded in a shielded environment. The results illustrate the resilience of the proposed training strategy. Therefore, our method contributes to the identification of specific brain activity in children who are taking part in the research as a result of their participation. On the basis of several parameters such as filtering response, accuracy, precision, recall, and F-measure, the MATLAB simulation software was used to evaluate the performance of the proposed system.

“…e pretrained models SqueezeNet [14], ResNet18, and its variants through transfer learning are explored through analysis of chest CTscans. Imbalanced datasets are dealt with using wavelets in combination with CNNs and the performance of the ResNet18 is found to be satisfactory [22]. e authors in [23] have presented a combinational model with RNN and CNN called the ProgNet model for observing the progression of infection in temporal images through time series; the model results were 92% accurate in detecting the infection progression.…”

Section: Related Workmentioning

confidence: 99%

An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution

Biradar¹,

Alqahtani

Nagaraj³

et al. 2022

The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients’ chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world.