Search

In the recent years, the carcinoma disease in women has significantly observed and it has also caused deaths as per WHO 2018 report of carcinoma statistics recorded approximately 2 lakhs registered cases and around 90000 reported deaths. The speed of survival has become very difficult at higher stages of growth and quite 45% of women's in India suffer from stage 3 and 4 of carcinoma. The target of this research is to deliver a report on carcinoma on the basis of the performance of Support Vector Machine [SVM] methodology and random forest using 5 folds, 10 folds, 20 folds with a training set size 50, 60, 70, 80 and 90 respectively. These techniques have achieved an accuracy of 94.5% and 98.40% through the cross validation of Support Vector Machine [SVM] and Random Forest [RF] method.

Lots of available biosensor are not portable for the detection of disease for that bulky instruments are required. To overcome these problems, we can use microstructure based biosensor. The sensor is Micro electro Mechanical Sensor (MEMS) based cantilever. Here, in this article simulating cantilever by changing chemical materials and parameters. For the different cancer diseases like Brest cancer cu(copper) and for heart cancer gold material were used to simulate cantilever. Laser beam and photo detector technology are bulky and costly also. Instated of that cantilever having dimension are width 1 micro meter, length 4 micrometer and height of cantilever is varies in between 25 to 50 nm which gives huge sensitivity. In this paper simulation of the cantilever based on various materials as per the changes in resistance and capacitance effect analysis, cancer tissues behaviour, and identify the exact state of cancer.

Hyperspectral face recognition is a very challenging task as well as time-consuming process. Hyperspectral face images (HFI) are a very big size images as compare to normal RGB images. HFI is always more than 10 bands with spatial resolution and its size varies from camera to camera. To execute these large numbers of files, a big memory is required due to high dimensions. Principle Component Analysis is used with double layer feature extraction to reduce the dimensional size without losing the prominent features. Double layer PCA is applied on the Hong Kong Polytechnic University’s Hyperspectral Face Database (PolyU-HSFD) and classify on the basis of k-nearest neighbor.

In this paper presents biometrics based fingerprint and palmprint recognition and authentication system. Fingerprint and palmprint images are enhanced using preprocessing techniques such as morphological operations. The feature extraction techniques such as neighborhood operation and FAST feature algorithm is used to independently extract fingerprint and palmprint features. These techniques are more reliable and faster than traditional techniques used. Experimental results shown recognition rate 89.29% for fingerprint and 100% for palmprint this implies that the proposed methodology has better performance and is more reliable over the techniques proposed and used earlier. Abbrevations SF Single Flat SFF Single Flat Flexi LT Left Thumb finger LI Left Index finger LM Left Middle finger LR Left Ring finger LL Left Little finger

Dorsum of the hand can be very useful in Personal identification but yet it has not that much extensive attention. Single scan of dorsum hand can give two biometric traits finger-knuckle and finger nail. This paper presents an approach to combine Finger-knuckle and finger-nail features. Finger nail biometric is considered as quite unique biometric trait hence we combine this trait with finger knuckle. We also developed an algorithm to extract ROI from Finger-knuckle and finger-nail. Finger- knuckle features are extracted using Mel Frequency Cepstral Coefficient (MFCC) technique and the features of finger-nail are extracted from second level wavelet decomposition. We combined these features using feature level fusion and feed forward back-propagation Neural Network for classification. The performance of the system has been tested on our own KVKR- knuckle database that includes 100 subject's dorsal hands. Evaluation results shows that increase in training set gives increased performance rate. The best performance of the proposed system reaches up to 97% with respective training of 90% of total dataset. 

Biometric is emerging area in the computer science for the secure various systems. Day to day life peoples are preferred to use, robust and highly acceptable security system which can surpass the human errors. Many scientists are engaged to develop a strong biometric system, but there are a lot of challenges in the real time application. It is observed and found that researchers are only working on too old laboratory databases such as Olivetti Research Laboratory (ORL). But now a day's various cost effective data acquisition sensors are coming on the market with high resolution of the data. When we are using a different type of data capturing devices gives the difference in performance of recognition rate. In this work we have proved that recognition rate is affected by the various sensors as well as database environment. For robust face recognition system suitable algorithms are suggested to different type of sensors.

Day to day life is more unsecure as per the hacking is concerned. Our data is not secure because it can be stolen, hacked, destroy, manipulate, password may forget, guess, Card, token, etc. To overcome this problem biometrics is used as a strong authentication system. The person should be present at the time of the instance. Biometric security is challenging task in day to day life because it is difficult to avoid the fraud. In this research paper emerging biometric trait, i.e. Hyper Spectral Face is considered for human authentication system. There are various visible spectrum of electromagnetic spectral bands are considered for face recognition instead of only three RGB bands. Hyper Spectral gives band wise more finite detail information on face. It is very novel and more accurate than ordinary face recognition system. Hong Kong PolyU Hyper Spectral Face Database used for Face recognition. Kernel Principle Component Analysis (KPCA) algorithm gives prominent features of the Hyperspectral Face Dataset. Extracted features are classified by Mahalinobis Cosine (Mahcos) similarity measurement technique. The Recognition rate calculated on the basis of One Rank Level it furnishes 69.20%.

Brain is a vital part of our body, without brain can't perform any task. Human beings are termed as an intelligence because of the unique properties of the human brain. When we want to do any activity, it generates the signals. On the basis of brain signal our body act accordingly. The signal pattern of brain to give the instruction to the organs is varies person to person. Therefore, by using single electrode brain signal are used as biometrics security. Security is most important task of every individual, living organism and nation. Security means any record, things, any individual object keeping safe from harmful things or imposter. The researcher has developed various security systems, this area is developing with high speed. Now scientists are engaged to make most of the record secure using Biometric. Fusion of brain signal channels for security purpose using electroencephalography (EEG) and achieved better performance of recognition rates of a person to identify and verify a person.

As population grow, the task of fingerprint verification system becomes increasingly more difficult. However performance improvement of biometric based verification system is an interesting and emerging area. In fingerprint verification systems, there are usually multiple enrolled impressions for a same finger. The performance of the systems can be improved by combining these impressions at enrollment level as well as testing level. In this research paper we use a method of score level fusion using multiple enrollment and multiple testing impressions to achieve higher accuracy. Experimental results show that a larger improvement can be obtained by using fusion of multiple enrollments and one testing impression scheme than other one to one matching. Experiment conducted with FVC2000, FVC 2002 and our own (BAMU) database. For the evaluation performance measures (FAR), (FRR), Recognition rate (RR) Equal Error Rate (EER) and Receiver Operating Characteristics curve (ROC) has been used.

Compound character recognition of Devanagari script is one of the challenging tasks since the characters are complex in structure and can be modified by writing combination of two or more characters. These compound characters occurs 12 to 15% in the Devanagari Script. The moment based techniques are being successfully applied to several image processing problems and represents a fundamental tool to generate feature descriptors where the Zernike moment technique has a rotation invariance property which found to be desirable for handwritten character recognition. This paper discusses extraction of features from handwritten compound characters using Zernike moment feature descriptor and proposes SVM and k-NN based classification system. The proposed classification system preprocess and normalize the 27000 handwritten character images into 30x30 pixels images and divides them into zones. The pre-classification produces three classes depending on presence or absence of vertical bar. Further Zernike moment feature extraction is performed on each zone. The overall recognition rate of proposed system using SVM and k-NN classifier is upto 98.37%, and 95.82% respectively.

Handwritten Devanagari Compound character recognition is one of the new challenging task for the researcher, because Compound character are complex in structure, they are written by combination two or more character. Their occurrence in the script is up to 12 to 15%. In this research paper, a recognition system for handwritten Devanagari Compound Character is proposed bases on Legendre moment feature descriptor are used to recognize. Moment function have been successfully applied to many pattern recognition problem, due to this they tends to capture global features which makes them well suited as feature descriptor. The process image is normalized to 30X30 pixel size divided into zone, from this structural as well as statistical feature are extracted from each zone. The proposed system is trained and tested on 27000 handwritten collected from different people. For classification we have used Artificial Neural Network. The overall recognition rate for basic is up to 98.25% and for all compound character is 98.36%.

Over many decades lines on hands used for astrological and numerology analysis because there is a trust that Lines never lie. Dorsum of the hand can be very useful in personal identification but yet it has not that much extensive attention. Single scan of dorsum hand can give two biometric traits finger-knuckle and finger nail. This paper presents an approach to combine Finger-knuckle and finger-nail features. Finger nail biometric is considered as quite unique biometric trait hence we combine this trait with finger knuckle. Finger knuckle features are extracted using Mel Frequency Cepstral Coefficient (MFCC) technique and the features of finger-nail are extracted from second level wavelet decomposition. We combined these features using feature level fusion and feed forward back propagation neural network for classification. The performance of the system has been tested on our own KVKR-knuckle database that includes 100 subjects dorsal hands. Evaluation results shows that increase in training set gives increased performance rate. The best performance of the proposed system reaches up to 97% with respective training of 90% of total dataset.