Background :
In present scenario electricity theft is a major problem for government. This problem affects
Indian economy since GDP goes down due to theft. Electricity is a part of living and its theft effects the common man
because indirectly common man has to pay for the theft done by someone else in form of extra charges.
Methods:
In this paper, we present a novel identification pattern-based energy fault detector, by leveraging the customers'
normal and faulty line. The target of this paper is to implement a system to monitor the readings and to find the fault in
power line in real time. Manipulations in readings of meter are impossible.
Results:
There is a real time monitoring of meters. Readings could be provided to customers on daily, weekly, monthly or
yearly basis. As soon as there occur a fault, it could be rectified because detection is on real time basis and further
message can be sent to customer about the fault.
Conclusion:
A comparison of developed real time monitoring system is done with GSM meters and analog meters.
:
Mammography is the technique to detect breast cancer abnormal tissues using digital screening. It is the most efficient method to detect the cancerous tissues in the breast. But as the data for detecting, the abnormal tissue is very large, so it is a very inappropriate method for some radiologists to detect the abnormal tissues correctly. Therefore, computer-aided diagnosis is useful for detecting the cancerous tissues. For this, feature extraction and selection is considered an important and efficient method for mammogram classification of breast cancer. In this proposed work, the focus is made on wavelet family performance named db8 and bior3.7 used for extracting the features using GLCM feature extraction technique and 27 texture features are extracted at each level of decomposition and then the classification is done using different classifiers named Fuzzy-NN, Naive Bayes, MLP and Genetic programming. After this, the feature selection method is also applied to the extracted features named as PCA and Wavelet and then the comparison is made with different classification algorithms for both the wavelet family.
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