L M Patnaik

A Data Mining Approach for Data Generation and Analysis for Digital Forensic Application

With the rapid advancements in information and communication technology in the world, crimes committed are becoming technically intensive. When crimes committed use digital devices, forensic examiners have to adopt practical frameworks and methods to recover data for analysis which can pose as evidence. Data Generation, Data Warehousing and Data Mining, are the three essential features involved in the investigation process. This paper proposes a unique way of generating, storing and analyzing data, retrieved from digital devices which pose as evidence in forensic analysis. A statistical approach is used in validating the reliability of the pre-processed data. This work proposes a practical framework for digital forensics on flash drives.

Classification of Neurodegenerative Disorders Based on Major Risk Factors Employing Machine Learning Techniques

Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized for the classification of various diseases. Data mining technology provides a user-oriented approach to novel and hidden patterns in the data. The present study consisted of records of 746 patients collected from ADRC, ISTAART, USA. Around eight hundred and ninety patients were recruited to ADRC and diagnosed for Alzheimers disease (65%), vascular dementia (38%) and Parkinsons disease (40%), according to the established criteria. In our study we concentrated particularly on the major risk factors which are responsible for Alzheimers disease, vascular dementia and Parkinsons disease. This paper proposes a new model for the classification of Alzheimers disease, vascular disease and Parkinsons disease by considering the most influencing risk factors. The main focus was on the selection of most influencing risk factors for both AD and PD using various attribute evaluation scheme with ranker search method. Different models for the classification of AD, VD and PD using various classification techniques such as Neural Networks (NN) and Machine Learning (ML) methods were also developed. It is observed that increase in the vascular risk factors increases the risk of Alzheimers disease. It was found that some specific genetic factors, diabetes, age and smoking were the strongest risk factors for Alzheimers disease. Similarly, for the classification of Parkinsons disease, the risk factors such as stroke, diabetes, genes and age were the vital factors.

A Novel Data Generation Approach for Digital Forensic Application in Data Mining

With the rapid advancements in information and communication technology in the world, crimes committed are also becoming technically intensive. When crimes committed use digital devices, forensic examiners have to adopt practical frameworks and methods for recovering data for analysis as evidence. Data Generation, Data Warehousing and Data Mining, are the three essential features involved in this process. This paper proposes a unique way of generating, storing and analyzing data, retrieved from digital devices which pose as evidence in forensic analysis. A statistical approach is used in validating the reliability of the pre-processed data. This work proposes a practical framework for digital forensics on flash drives.

Classification and treatment of different stages of alzheimer's disease using various machine learning methods

There has been a steady rise in the number of patients suffering from Alzheimers disease (AD) all over the world. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. The patients records are collected from National Institute on Aging, USA. The Sample consisted of initial visits of 496 subjects seen either as control or as patients. Patients were concerned about their memory at the National Institute on Aging. It also consisted of patients and caregiver interviews. This research work presents different models for the classification of different stages of Alzheimers disease using various machine learning methods such as Neural Networks, Multilayer Perceptron, Bagging, Decision tree, CANFIS and Genetic algorithms. The classification accuracy for CANFIS was found to be 99.55% which was found to be better when compared to other classification methods. Based on the outcome of classification accuracies, various management and treatment strategies such as pharmacotherapeutic and non pharmacotherapeutic interventions for mild, moderate and severe AD were elucidated, which can be of enormous use for the medical professionals in diagnosis and treatment of AD.

Feature Level Fusion Based Bimodal Biometric Using Transformation Domine Techniques

Bimodal biometric used to authenticate a person is more accurate compared to single biometric trait. In this paper we propose Feature Level Fusion based Bimodal Biometric using Transformation Domine Techniques (FLFBBT). The algorithm uses two physiological traits viz., Fingerprint and Face to identify a person. The Region of Interest (ROI) of fingerprint is obtained using preprocessing. The features of fingerprint are extracted using Dual Tree Complex Wavelet Transforms (DTCWT) by computing absolute values of high and low frequency components. The final features of fingerprint are computed by applying log on concatenated absolute value of high and low frequency components. The face image is preprocessed by cropping only face part and Discrete Wavelet Transforms (DWT) is applied. The approximation band coefficients are considered as features of face. The fingerprint and face image features are concatenated to derive final feature vector of bimodal biometric. The Euclidian Distance (ED) is used in matching section to compare test biometric in the database, it is observed that the values of EER and TSR are better in the case of proposed algorithm compared to individual transformation domain techniques.

Proactive sliding -window strategy for merging sensor-enabled cars

It is de facto that merging two different streams of vehicles into one single stream poses some degree of inconvenience. As human lives are at stake, a lot of care has to be taken in this area of traffic management. In this paper a proactive merging strategy is proposed, keeping the intersection of ramp and a highway as the main focus. In our algorithm, it is assumed that the vehicles are sensor-enabled, with which they can communicate their position, velocity, acceleration and other dynamic attributes as needed by micro-electro-mechanical systems(MEMS), to each other. By doing so they can decide amongst themselves the order of merging at a safe distance well before the intersection. A sliding- window is used to monitor the vehicles on the main road as well as the ramp. For the vehicles in the window we consider the time they take to merge with minimum deceleration. We logically order them for merging into one single stream. Our experiments demonstrate that Sliding-Window Proactive strategy increases the traffic throughput on highways and decreases the delay.

Probabilistic Model for Single and Multi-Sensing Intrusion Detection in Wireless Sensor Networks

Wireless Sensor Networks consists of tiny devices capable of processing, routing the sensed data and are capable of detecting the intruders. The process of detecting any suspected(anomalous) moving object(attacker) within the reach of a Wireless Sensor Network area is referred to as intrusion detection. In this paper, we propose an algorithm to detect the intruder by the cluster heads in a 2D and 3D homogeneous Wireless Sensor Networks. This algorithm overcomes the attacks on implementation and also, reduces the energy consumption. The proposed algorithm considers Single Sensing and Multi-Sensing Intrusion Detection using minimum number of sensor nodes and a probabilistic model has been developed for both 2D and 3D homogeneous networks. Simulation results show that the power analysis attack and energy consumption is minimized by activating only few sensor nodes for detection and using only few sensor nodes for processing of data. The performance of the proposed algorithm is better compared to using all the sensor nodes for detection where the energy consumption is more.

Multiple Dynamic Sinks to Maximize the Lifetime and Conservation of Energy in Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of battery operated tiny sensor nodes and connected in a network for communication. Improving the lifetime of sensor network and energy conservation are the critical issues in WSNs. Nodes closer to the sink node drains their energy faster due to continuous and larger transmission of data towards a sink node. Dynamic Sinks solve the problem of lifetime and energy in WSNs. It moves dynamically to particular positions among the different positions in a predetermined order to collect data from sensor nodes. There is a considerable delay in the case of single mobile sink. In this paper we use the concept of multiple Dynamic sinks to collect data in different zones which in turn coordinate to consolidate the data and complete the process of receiving data from all the sensor nodes. A distributed algorithm synchronizes all dynamic sinks and it is used to reduce delay in consolidation of data and reduces the overall energy consumption. This twin gain increases the lifetime of wireless sensor network and it reduces delay. Simulation results using multiple dynamic synchronized Sinks clearly show that there is an improvement of the lifetime and energy conservation of wireless sensor networks in comparison with single mobile sink and static sink.

FKP biometrics for human authentication using Gabor Wavelets

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like Finger Knuckle Print (FKP) of a person is unique and secure. In this paper, we propose a human authentication system based on FKP image of a person. We apply Gabor Wavelet on pre-processed FKP image. Then we identify the peak points in Gabor Wavelet graph. The successive distances between those points are calculated and are stored in a vector. Now, the elements in distance vector stored in database and that of input image are compared. Such a match is considered to be success if the difference between two such elements is lesser than the threshold value. Now, the probability of success is computed. The person is authenticated based on the value of computed probability. The proposed system has the FAR of about 1.24% and FRR as 1.11%.

Real-Time Link-Reliability Routing for QoS in Wireless Sensor Networks

This paper proposes a Real-Time Link Reliability Routing protocol for wireless sensor networks (WSNs). The protocol achieves to reduce packet deadline miss ratio while considering link reliability, two-hop velocity and power efficiency and utilizes memory and computational effective methods for estimating the link metrics. Numerical results provide insights that the protocol has a lower packet deadline miss ratio and improved sensor network lifetime. The results show that the proposed protocol is a feasible solution to the QoS routing problem in wireless sensor networks that support real-time applications.