Texture Based Malware Pattern Identification and Classification

Malware texture pattern plays an essential role in defense against malicious instructions which were analyzed by malware analyst. It is identified as a security threat. Classifying malware samples based on static analysis which is a challenging task. This paper introduces an approach to classify malware variants as a gray scale image based on texture features such as different patterns of malware samples. Malicious samples are classified through the machine learning techniques. The proposed method experimented on malware dataset which is consisting of large number of malware samples. The similarities are calculated by texture analysis methods with Euclidian distance for various variants of malware families. The available samples are named by the Antivirus companies which can analyze through supervised learning techniques. The experimental results show that the effective identification of malware texture pattern through the image processing which gives better accuracy results compared to existing work

Computer Aided Diagnosis - Medical Image Analysis Techniques

Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties

Mammography Image Enhancement using Linear, Nonlinear and Wavelet Filters with Histogram Equalization

In the worldwide, breast cancer is one of the major diseases among the women. In the modern medical science, there are plenty of newly devised methodologies and techniques for the timely detection of breast cancer. However, there are difficulties still exist for detecting breast cancer at an early stage for its diagnoses because of poor visualization and artifacts present in the mammography. Thus the Digital mammographic image preprocessing often requires, enhancement of the image to improve the quality while preserving important details. The proposed method works in three stages. First it removes all the artifacts present in the image. Second it denoise the image by using Linear, nonlinear and wavelet filters. Third, contrast of the image increased by histogram equalization. This method definitely helps to computer aided diagnosis system to increase the accuracy. The experimental results are tested on two standard datasets MIAS and DDSM.

Enhancing Face Recognition through Dimensionality Reduction Techniques and Diverse Classifiers

Face recognition is essential component of various applications including computer vision, security systems and biometrics. By examining the efficacy of several dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), and Non-Negative Matrix Factorization (NMF), this paper offers a novel approach to face recognition. These techniques are combined with diverse classifiers, including Support Vector Machines (SVM), Random Forest, LightGBM, and k-Nearest Neighbors (KNN), are employed to evaluate their impact on face recognition accuracy. Experiments were conducted on Olivetti faces data set. We have demonstrated the comparative analysis of different dimensionality reduction techniques classifiers in terms of accuracy, precision, recall, f1score. Results shows the potential of integrating PCA with diverse classification models to enhance face recognition accuracy and highlights its applicability in real-world scenarios.&nbsp

GSM Based SMS Alert Fire Alarm System

Abstract: The design of a fire alarm with Arduino-based system by means of GSM Module. The work purposely for house safety where the main point is to avoid the fire accidents occurred to the residents and the properties inside the house. In order to prevent losses accrued from fire accidents, various alarm systems have been developed such as smoke detectors, temperature sensor based systems etc. The design and implementation of a cost effective and reliable GSM based SMS Alert fire alarm system. The device will be able to monitor the temperature of the environment, the smoke level, send SMS alert to an inbuilt GSM number. When the system detects the temperature of 100C or more, it will immediately display an alert notification on LCD display and simultaneously sending an SMS alert to the users upon the high raise temperature in the house. This fire detection system consists of a smoke sensor, buzzer, LCD display and GSM module is interfaced with Arduino board. Keywords: Smoke sensor, GSM Module, Arduino, LED display, Buzzer</jats:p

Computation Pre-processing Techniques for Image Restoration

Image restoration is to enhance the image quality which is blurred and noised from various defects which damage the quality of an image. The most degradation is done in motion blur and noise defects as shown in the results. This introduces and implements the computing methods used in the image processing world to restore images as well as improve the quality by threshold. In order to know the detailed information carried in the digital image for better visualization. The aim is to provide information of image degradation and restoration process by various filters such as wiener filter, blind convolution and wavelet techniques are used in experiments in this paper will be presented as followed by MATLAB simulation results. Weiner filter gives maximum PSNR value and minimum MSE value in dB comparable to other techniques for image restoration