The given paper analyses the basics of the processes that make it possible to achieve the sensory immersion based on the combination of the newest technologies in the sphere of audio and the active design of the visual stage. Our approach to testing the quantitative approach of immersive experiences is the interaction of spatial audio systems, adaptable lighting structures, and physical changes to the stage. Through controlled experiments of 240 participants in 12 different performance settings we formulate mathematical models of the correlation between technological variables with measurable immersion metrics. This is shown to be a key indication of increasing sensory involvement by our result that the so-called heavy plasticity of stage appearance that is marked by the ability to dynamically and malleably modify both visual and spatial items is particularly important when it is accompanied by multi-dimensional sound fields. The paper illustrates that, the immersion intensity (I) has a power-law relationship with technological integration density (0 tech ) and sensory coherence (Cs ), which are represented by the equation I = k 0 tech a Csb where a = 1.37 and b = 2.14. The results suggest that maximum immersion is possible when the use of sensory stimuli is 6.8 per second and the spatial coverage of audio is more than 85% of the performance area. The present study offers empirical bases of development of next-generation immersive experiences in the entertainment, virtual reality, and therapeutic applications.

Internet of Things (IoT) networks have established unparalleled connection possibilities and convenient features and have set new challenges associated with dubious security barriers and potential attacks. This study evaluates attack detection performance of LSTM-Based Variant models namely LSTM, DeepBiLSTM and BLSTM recurrent neural networks by bringing up experiment analysis of the IoT networks. We evaluate the three models using benchmark IoT dataset in terms of detection accuracy, precision, recall and F1 measure. The experiment results indicate that the three LSTM-based models, LSTM, BiLSTM, and DeepBiLSTM, demonstrate a high level of performance, regardless of the batch size (32, 64, 128, 256, and 512). DeepBiLSTM has a little better overall performance, which validates its soundness and suitability towards scale in detecting attacks in large IoT networks.

A hybrid GAN architecture was designed to solve multi-modal medical image synthesis and disease classification by combining key architectural principles from DC-GAN, Conditional GAN, and SR-GAN, thereby enhancing training stability, providing label-conditioned image synthesis, and improving perceptual image quality. The proposed framework was extensively tested on large, diverse medical imaging datasets, including chest X-ray images to identify pneumonia, retinal fundus images to evaluate diabetic retinopathy, brain MRI images to detect tumors, microscopic images of leukemia white blood cells, and dermoscopic images to analyze skin cancer. Quantitative experimentation revealed a steady convergent behavior, the values of the generator loss and discriminator loss continued to decline throughout each of the datasets and the lowest values were found in diabetic retinopathy cases (generator loss of 0.522 and discriminator loss of 0.425) and leukemia cases (generator loss of 0.285 and discriminator loss of 0.224), maintaining the presence of diagnostically significant pathological features. Computational efficiency was also high, with about 0.75 hours of training time and a relatively small number of 0.67 million parameters, compared to SR-GAN-based models that require more than 10 hours of training time and more than 2.3 million parameters. The success of the framework was also confirmed by the quality of the generated images, attaining a high signal-to-noise ratio of 36.742, structural similarity index of 0.93, and Fréchet inception distance of 30.402, which is better than several other recent state-of-the-art methods, such as DRForecastGAN, GAN-VSP, IFGAN, and Pix2Pix GAN. Also, incorporating a Squeeze-and-Excitation convolutional neural network classifier not only led to a significant boost in disease classification performance but also improved the accuracy of diabetic retinopathy and pneumonia to 0.90 and 0.98, respectively. In general, the suggested hybrid GAN model has great potential as a low-cost, high-quality solution for medical image generation, data augmentation, and automated disease detection in clinical decision-support systems.

This paper focus on DC-DC converter called Novel Cascaded topology. This converter lies in the category of Non-Isolated DC-DC boost converters with upgraded voltage gain based on conventional topology with LD cell for micro-source grids. The proposed novel cascaded topology increases the voltage boost ability. There are some merits of our proposed topology that includes small voltages stresses across the switch, as well as upgraded gain. We prefered to use switch having small voltage rating as well as small resistance in ON-mode (RDS-ON), it leads to provide high efficiency. In our cascaded topology with LD cell,we did not use large duty cycle, coupling inductors as well as transformer.We will utilize the Continuous Conduction Mode (CCM) for the analysis of proposed novel cascaded topology. This topology is designed to operated on 12V supplying voltage, 55% duty cycle, 265W output power and frequency of 12KHz. The continous conduction mode (CCM) has been analyzed theoretically as well as practically based derived equations. The novel cascaded converter has been simulated in PSIM as well as MATLAB/SIMULINK, while explanations of the overall results are provided based on PSIM.

World’s second most occurring cancer is breast cancer. Prediction of disease is one of the most challenging tasks and there are many factors that effect this type of diagnosis like the ability of visual perception. This paper proposed a Convolutional Neural Network (CNN) based proper method for analyzing the earliest signs of breast cancer with the help of mammogram images. The main goal of proposed system is to identify the disease of breast cancer at early stages. Due to this reason, Mammographic image analysis society (MIAS) dataset is used. There are three hundred & twenty two (322) mammograms in the dataset, with 209 images of normal breasts and 133 images of abnormal breasts. While abnormal breasts are further classified as benign (62 images) and malignant (51 images). To implement this system, python library Keras and Tensor Flow libraries are used along with deep learning model CNN. Convolutional Neural Network (CNN) has been shown to be effective in detecting breast cancer in mammography images with 70% accuracy rate, according to promising testing data. The proposed system will enable the radiologist in detecting breast cancer in early stages.

Coronavirus is proved to be a severe epidemic diseasethroughout the world. Despite of endeavoring lot of medicalfacilities for mitigating with this pandemic, still the number ofinfected cases increased rapidly, which leads to lack of healthcareresources (i.e. hospitals, doctors and other healthcare amenities). Early stage risk prediction by analyzing several clinical andbehavioral risk factors is considered to be a promising solutionfor prescribing appropriate triage to patients and to reduce themortality rate due to this fetal disease. To cope up with thisproblem, in our study we have proposed a deep learning basedapproach for the early stage prediction of risk of infection andrisk of mortality in individuals possessing certain risk factors. Wehave utilized a publically available covid-19 dataset incorporatingseveral risk factors that may cause this infection. For the selectionof most significant risk factors i.e. with respect to their level ofimportance in risk prediction, we have employed three featuresselection techniques (i.e. f_classif, PCA and Tree). The set ofextracted features are the utilized for the training of proposed ANN for the prediction of infection risk and mortality risk due tocovid-19. For the performance analysis of proposed method, fourdifferent evaluation metrics are being employed including: MSE,MAE, ME and EV. The proposed model has achieved a minimum loss (MSE) of 0.00137 for infection risk prediction and MSE of0.000012 for mortality risk prediction.

The approach for this project is simple. For the detection of moving, we had employed the method of image subtraction and absolute frame differencing of consecutive frames coming from the static camera for making a robust approach for signature locking of the detected target. Through the image subtraction technique, we extract the coordinates of the moving target from the image and to a designed mechanical system that can operate the gun. A red signature is accurately a red object in the real-time video. The reason behind a red object is that a thermal camera detects the object on the base of the temperature gradient and the thermal camera detects an aircraft in the form of a red object. The thermal camera is so expensive that why we chose the visual camera and selected red object as a signature so that this algorithm is also capable to detect and track a thermal target from the thermal camera. And after detecting the coordinate of the required target sent to our gun barrel to shoot that target for future enhancement. There exists a lot of applications for our project, especially from a security point of view. This thesis explained how to detect and track red targets in a live video. There are distinctive ways to deal with recognizing the red/warm shading in each edge. One of these prevalent methodologies incorporates the change of the entire RGB outline into comparing the HSV (Hue-Saturation-Value) plane pursued by extraction of pixel esteems just for red. One can pick a range that mirrors every single existing shade of red to distinguish and separate all hues in the frame.