M. Meena

Communication plays a major role in human’s life. Without network communication can’t be done, to achieve proper and effective communication different generations of networks are introduced. Each generation has its own features and perspective of communication, but till now there is no network properly makes people to communicate. Many researches says 5G network will rule the network world as it satisfies all the effective network goals. This paper is proposed to obtain all the goals of a communication network by making proper handover with the help of machine learning.Here we have used two main algorithms to make our 5G handover process by clustering and classifying. Clustering is a process of making the datasets into single units of every users and classification is a process of classifying user’s clustered datasets into common path using prediction and forecasting. For clustering we are using K-means and for classification we are using Random Forest algorithm. By using the algorithms the datasets which is being predicted and forecasted is stored in the cloud. Here cloud technology is used as a platform for developing datasets associated with internet. 5G network adapts to any form technology easier and here we have used all the essential technologies under machine learning. This paper deals with all the above methodologies effectively with newer combinations of algorithms along with proper solutions.

Visibility is a big issue in imaging through fire and smoke, a situation often encountered by firefighters. Emission from fire is significantly higher in intensity compared to the light reflected from an object obscured by fire, leading to a drastic reduction in the signal-to-noise ratio for visualization. On the other hand, the presence of smoke scatters light in random directions, further reducing visibility. By implementing quadrature lock-in discrimination algorithm on the images captured by a sCMOS camera in the presence of a modulated blue light source and blue filter, we report a significant improvement in the image contrast measured for an object in the presence of flame and smoke. Our methodology is straightforward to realize and facilitates reliable identification of objects that are otherwise concealed in real-life situations due to poor visibility.

Reduced visibility is a common problem when light traverses through a scattering medium, and it becomes difficult to identify an object in such scenarios. What we believe to be a novel proof-of-principle technique for improving image visibility based on the quadrature lock-in discrimination algorithm in which the demodulation is performed using an acousto-optic modulator is presented here. A significant improvement in image visibility is achieved using a series of frames. We have also performed systematic imaging by varying the camera parameters, such as exposure time, frame rate, and series length, to investigate their effect on enhancing image visibility.

We implement, using a coherent source, a coined quantum walk of light in frequency space in a tabletop experiment utilizing a series of modified Michelson interferometers that incorporate polarization optics and acousto-optic modulators. Manipulating the phase of the radio frequency that governs the acousto-optic interaction, we achieve symmetric or asymmetric quantum walks. Introducing rapid random phase shifts electronically, while regulating the amplitude, we cause controlled dephasing and thus simulate a gradual transition from the quantum walk to the classical random walk.

As a result of immense innovation, the measure of information is expanding day by day. This data is used by Internet users who also provide their feedback. They describe the product in detail and evaluate the sentiment of the product. It is essential to explore and analyse their reviews for a better decision-making. For this, we use sentiment analysis process. Sentiment analysis, which is also called as opinion mining is a natural language processing technique which extracts the information and identifies the users’ views as positive or negative. This paper provides a review of sentiment analysis, its challenges, issues and also a survey of different approaches and techniques to handle those issues with respective advantages and disadvantages. A comparative study of different approaches has also been included.

We experimentally demonstrate binary phase-shift keying and multi-stage four phase shift keying of a microwave carrier and its corresponding demodulation in the optical regime using room temperature Rb atoms. We use a cyclic three-level scheme in 85Rb atoms to achieve this. The importance of our scheme is that the cyclic, closed interaction of the atomic levels with electromagnetic fields makes our system inherently sensitive to the phase of the microwave field. This enables our system to directly encode a phase modulated digital signal in the microwave field and decode it as intensity modulation in the optical field. We measure the correlation of our demodulated optical signal with an ideal template and establish a viable signal bandwidth of about 1 MHz. Our atomic scheme also enables phase dependent amplification of the demodulated optical signal through a hybrid second order nonlinearity. This phase dependent atomic antenna has inherent features of demodulation, radio-to-optical conversion, and amplification. The ground states used in our scheme are quantum memory storage spin states, which makes our scheme inherently suitable for applications involving communication and storage and retrieval of quantum signals.

We report experiments conducted in the field in the presence of fog, that were aimed at imaging under poor visibility. By means of intensity modulation at the source and two-dimensional quadrature lock-in detection by software at the receiver, a significant enhancement of the contrast-to-noise ratio was achieved in the imaging of beacons over hectometric distances. Further by illuminating the field of view with a modulated source, the technique helped reveal objects that were earlier obscured due to multiple scattering of light. This method, thus, holds promise of aiding in various forms of navigation under poor visibility due to fog.

Due to elementary requisite and amplified figure of unwanted spam in network has gain high attention of investigators. A worthy amount of investigators have shown several causes of spam in network. Therefore to overwhelm the issues of handy prediction schemes of spam, a dissimilar and efficient ensemble approach has been proposed in this study that utilized the functionalities of multilayer perceptron (MLP), a form of neural network in the company of Bayesian theorem. To examine and reveal the competence of the proposed approach, it has coded in the most popular and employed open-source programming language JAVA. For signifying the effectiveness of the developed model, four different sets of data for spambase have opted from open-source database library of UCI. The weighty outcomes of the proposed system have proven its efficiency over the separate version of prediction method as well as current innovative accessible techniques.

Abstract There is a vast range of gases which get ionized, produce electron-ion pairs, on the passage of high energy charged particles. Such gases are extensively used in experiments such as LHC, Belle-II, RHIC, DAFNE, etc which produce high energy particles. Panjab University established a detector assembly and characterization laboratory dedicated to gaseous detectors such as Resistive Plate Chamber (RPC) and Gas Electron Multiplier (GEM) for the LHC experiment. Here, we present the recent work on the fabrication and characterizations of the GEM detector at Panjab University.

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative framework with naive batch sequential active learning approach is applied to improve the clustering performance. The iterative framework requires repeated reclustering of the data with an incrementally growing constraint set. To address incrementally growing constraint set, a batch approach is applied which selects a set of points based on query in each iterative. In the iterative algorithm, k instances select the best matches in the distribution, leading to an optimization problem that term bounded coordinated matching. Leveraging the availability of highly-effective sequential active learning method will improve performance in terms of label efficiency and accuracy with less number of iterations.

As the innovation is developing immeasurably, the data is increases daily. This is used by the reviewers to identify the views regarding a particular thing and accordingly they decide their opinion on the basis of that reviews. Knowledge mining techniques has been used for extraction of elements from these datasets. The algorithm generated and tested can be used to find out technical words; hence we can expose them to appropriate class of technical words. Here we are only considering technical words in English language. We compare the results with the proposed approach. We calculate accuracy with random forest approach as well as bagging and find out that bagged random forest approach with Gini index for feature selection, i.e., proposed approach gives best result. With proposed approach, accuracy 80.64% in case of percentage split using 66 and 34 as training and testing percent and 86.81% in case of cross-validation model.