In the world of 20 billion connected devices, the data centers are just a drop in the ocean for the total energy consumption compared to the wireless access that is required for them. In the past years, it never became possible to obtain unprecedented bandwidth, to keep our data safe and repurpose the energy that we use for illumination to provide wireless communication, but now in the modern era, Light Fidelity (Li-Fi) can offer these things and much more. LiFi points towards high-speed, bi-directional, and networked wireless communications with the assistance of light. It offers users an almost similar experience as traditional wireless communications except using the light spectrum. With LiFi, a world connected by light has become an unbelievable reality. The design and functionality of the previous models are targeted to provide wireless communication but, to prove better quality results, an entire system is checked, and a new model is put forward. In this research study, the entire system is examined and tested on different values with various transmitting angles, data rates, distances, frequencies, the responsivity of PIN diodes to ensure the accuracy and best possible results are achieved. Testing of numerous ambient sources and modulation schemes aided in the success of this advanced technology. Different filters, transmitted pulses, modulation schemes, and angles are also changed and analyzed to study the behavior of LiFi thoroughly, which helped to attain a high-quality factor (Q-factor). The objectives of decline in Bit error rate (BER), increase in data rate, and quality signal are successfully achieved in the conclusion of this model. The research article is not only optimized but also has advanced features which mainly helped in gaining maximum results and goals behind this project.

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost.

Deep learning has brought substantial progress to medical imaging, which has resulted in continuous improvements in diagnostic procedures. Through deep learning architecture implementations, radiology professionals achieve automated pathological condition detection, segmentation, and classification with improved accuracy. The research tackles a rarely studied clinical medical imaging issue that involves bullet identification and positioning within X-ray images. The purpose is to construct a sturdy deep learning system that will identify and classify ballistic trauma in images. Our research examined various deep learning models that functioned either as classifiers or as object detectors to develop effective solutions for ballistic trauma detection in X-ray images. Research data was developed by replicating controlled bullet damage in chest X-rays while expanding to a wider range of anatomical areas that include the legs, abdomen, and head. Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. Multiple computational systems were used to verify the results, which showcased the effectiveness of the proposed solution. This research provides new perspectives on understanding forensic radiology trauma assessment by developing the first deep learning system that detects and classifies gun-related radiographic injuries automatically. The first system for forensic radiology designed with automated deep learning to classify gunshot wounds in radiographs is introduced by this research. This approach offers new ways to look at trauma which is helpful for work in clinics as well as in law enforcement.