The main focus of this paper is on obtaining Unique Device Fingerprint using JavaScript specifically Angular JS and Client JS techniques which together can make the comparative analysis more efficient and valuable. The term ‘Device Fingerprinting’, is a phenomenon to collect information such as Device name, java installed and its version, user agent, browser version, etc of an individual computing device for the purpose of identification. Through this technique even if the cookies are turned off we can obtain individual device fingerprints. Device Fingerprinting is often immutable and tends to rapidly change, making it challenging to get a unique one. The majority of solutions available to obtain a unique device fingerprint are complex, and most solutions don’t have the sufficient efficiency to obtain the required. Thus, this Comparative Analysis to obtain Unique Device Fingerprint using JavaScript techniques simplify this challenge and create an opportunity in analysing and obtaining the data in much more efficient way.

Object detection is one of the essential applications in the field of the computerized visualization that has been the major area of the research. OD is the technological aspects that identify the semantic entities of the class pictures. OD searches the object in real –time world by making the use of the object simulations that is called as the priori. Presently, one of the significant and simulating problems in computer vision seeks to locate the object features from large number of the already defined classes in natural pictures. An object detection scheme searches the objects of the real time available in digital pictures or videos , when the objects is related to any group of objects specifically cars, humans and so forth. Currently, the multi-class SVM is trained through MSRC dataset. Various papers have been studied in object detection scheme that has been found in various problems like (i) Illumination (ii) complex (size and shape) images. In existing research work, a hybrid genetic transformation algorithm was used for optimization and detection of the salient object features below different environment factors and acquired accuracy rate 86.1 Per cent. In research work, the three different types of problems are resolved using MSVM with PSO classification method, the real time images are segmented into various group of the classes or the sets. In the proposed system, SURF algorithm has applied for the extraction of the feature sets. In addition, PSO optimization algorithm has used for the instance selection of the essential characteristics based on classes from extracted feature metrics. The simulation outcome on MSRC data set defined the system performance of the improved model, mainly used for the small object detection in large size images. This proposed method has easy to detected the object of the image and improved the performance and overall performance has achieved the parameters values like accuracy score 98.4 per cent, False Acceptance Score 0.0046 %, False Rejection Score 0.0100 % and Mean Square Error Score 0.0023% as compared with existing techniques.

Machine learning is the ability of a computer to learn to make decisions without being programmed explicitly. It has evolved to artificial intelligence (AI). Machine learning focuses on making such computer programs that have the ability to change when in contact with new data. Data prediction and learning from data is the main task of such programs and machine learning helps to explore the study and construction of algorithms which help to do the same. We are witnessing rapid advancements in this field and it is a possibility in the near future that voice interaction systems will replace our normal way of interaction through standard keyboards in the near future. Today, some notable examples in voice interaction systems are Microsoft Kinect and Apple SIRI which perform really well, but, like any other technology, there is a wide scope of improvement in every speech system that is available today. They have their own drawbacks and continuous research is being done to increase the performance of such systems. One method to increase the performance of speech systems is using preprocessing like gender recognition. The paper discusses automatic gender identification systems using acoustic properties of speech. The different samples which have been processed using acoustic analysis and then applied to different machine learning algorithms, to learn gender-specific traits, are being discussed and experimented.