Lung cancer is a significant public health concern, and early identification can improve patient outcomes. Advanced machine learning approaches can increase the precision of computer-aided diagnostic (CAD) systems that use medical pictures to diagnose diseases, such as CT scans, which can help find lung cancer. In this paper, we examine the usage of image analysis and CAD while also proposing an unique method for predicting lung cancer using Convolutional Neural Network (CNN) approaches. Our study utilized a dataset of lung CT scans from patients with and without lung cancer. The lung regions' important characteristics were extracted from the preprocessed pictures using segmentation and feature extraction techniques. After that, we used these variables to train a CNN model to identify the likelihood of lung cancer. Our paper demonstrates the potential of CNN techniques and image analysis in predicting lung cancer with high accuracy rates. The suggested method might be utilized to create more precise and efficient CAD systems for diagnosing lung cancer, possibly resulting in early identification and better patient outcomes.Further research is required to examine the clinical applications of this technique and to confirm these findings in bigger datasets.

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Cognitive Femtocells have been standardized suitably to the technical framing of the Fourth cohort compact project to place them inside and outside the cell. Cognitive femtocells expand the coverage area and meet the future demands of higher data rates. However, as a result of the massive deployment of cognitive femtocells, users experience additional delay and unnecessary deliveries. The different hand off mechanisms are 1. Hard handover (break before make) 2. Smooth or soft handover (make-before-break). This can seriously affect the quality of service (QoS) of jam sensitive applications, such as Voice over long-term evolution (VoLTE). The 4GPP LTE-A / LTE-UE wireless networks aim to provide uninterrupted movement and rapid transfer pillar for (Real Time) RT and non-RT application services under the giant vigour. The prediction of mobility is an effective technique to identify a domestic NodeB (eNB / HeNB) evolved in the future and improve the overall service quality of the network and satisfy the end user experience. The different hand over mechanisms are, the first sense of a difficult delivery or transfer is one in which an breathe link should be penetrate ahead a unused one is created. The second new 3G technologies use CDMA where it is possible to have adjoining cells at the same frequency and this opens the odds of boast a transfer or transfer from where it is not required to repair the connection. This is called soft transfer, and is defined as a handover in which a not used tie-in is established before the used one is released. The third type of delivery is called smoother delivery or transfer. In this case, a pristine signal is added or deleted from the spry signal group. It can also happen when a signal is replaced by a burly signal from another sector under the base station. This type of transfer is available within UMTS and CDMA2000. “The cognitive femtocell will do in the delivery mechanism is that it will detect the new channel to transmit the data. With this we can avoid the delivery handover mechanism”. This study investigates the role of mobility prediction in reducing the end-to-end delay of VoLTE and the delay of handover under different user equipment (UE) speeds in mixed femtocell and macrocell environments. We propose a mobility based forecasting scheme based on the user path and measurements of the received signal reference signal and the quality reference signal (RSRP / RSRQ) with mixed RT traffic and not RT and then estimated using a network model new. The survey analysis shows that the proposed scheme will reduce the delivery delay by 35% to keep VoLTE at the end of the delay.

The wireless technology and communication plays a vital role in our daily life. The end users are expecting more Quality of Experience (QOE) rather than the Quality of Service (QOS). In order to provide full signal coverage the entire cellular network coverage is divided in to small cells called as femtocells, those femtocells are covered with femtocell antennas which are very small in size compared with regular antennas. With these femtocell coverage problem is solved but when a user moves from one location to another location the user has to switch from one base station to so many base station which cannot be maintained with present handoff methods. The present hand off methods working on distance calculation approach, the proposed method is based on the velocity and device direction calculated based on GPS location toward the Base Station (BS) of the device which may ping pong handoff effect.