PUBLISHING SEARCH LOGS PRIVACY GUARANTEE FOR USER SENSITIVE INFORMATION

Search Engine companies maintain the search log to store the histories of their users search queries. These search logs are gold mines for researchers. However, Search engine companies take care of publishing search log in order to provide privacy for user’s sensitive information. In this paper we analyze algorithm for publishing frequent keywords, Queries, and Clicks of a search log. Before Zealous algorithm, we discuss how different variants of anonymity failed to provide good utility (publishing frequent items) and strong privacy for the search logs. And also this paper includes how zealous algorithm provides good utility and strong privacy for publishing search logs

Studies on Tensile Characteristics of Kevlar/Jute/ Syntactic Foam Hybrid Sandwich Composites

In this study, a structured approach combining Taguchi experimental design and analysis of variance (ANOVA) is used to investigate the effects of skin material choice, material density, and percentage of reinforcement on the tensile properties of Kelvar/jute/synthetic foam hybrid sandwich composites. By deliberately changing these variables and examining how they affect tensile strength, modulus, and other important qualities, the goal is to maximize the mechanical performance of these composites. This work gives helpful insights into the interaction of these variables and their contribution to the overall tensile behavior of the composites through a series of carefully planned experiments and statistical studies. While ANOVA aids in quantifying the importance of individual components and interactions, the Taguchi approach makes it easier to identify the ideal parameter values. Making a substantial addition to the field of materials science and engineering, this combined method provides a solid framework for improving the design and engineering of lightweight, high-strength sandwich composites with customized features

Lead-DBS v3.0: Mapping Deep Brain Stimulation Effects to Local Anatomy and Global Networks.

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics

Oral Malignancy Detection Using Color Features from Digital True Color Images

One of the most prevalent forms of cancer worldwide is oral cancer which has a high rate of mortality. Diagnosis and treatment of oral premalignant lesions at an early stage reduces the death rate. The objective of this work is to detect malignancies by analyzing color features of digital true color oral images. A dataset of around 433 oral lesion images has been created that includes benign, premalignant and malignant lesions. The proposed method was experimented on this dataset. Different classifiers have been trained using various color features. The neural network classifier detects abnormalities with an accuracy of 94.82%. Results indicate that the color features have better potential in identifying benign and malignant oral lesions. </p

Oral Malignancy Detection Using Color Features from Digital True Color Images

One of the most prevalent forms of cancer worldwide is oral cancer which has a high rate of mortality. Diagnosis and treatment of oral premalignant lesions at an early stage reduces the death rate. The objective of this work is to detect malignancies by analyzing color features of digital true color oral images. A dataset of around 433 oral lesion images has been created that includes benign, premalignant and malignant lesions. The proposed method was experimented on this dataset. Different classifiers have been trained using various color features. The neural network classifier detects abnormalities with an accuracy of 94.82%. Results indicate that the color features have better potential in identifying benign and malignant oral lesions

An Ensemble Deep Neural Network Approach for Oral Cancer Screening

One of the ways to reduce oral cancer mortality rate is diagnosing oral lesions at initial stages to classify them as precancerous or normal lesions. During routine oral examination, oral lesions are normally screened manually. In a low resource setting area where there is lack of medical facilities and also medical expertise, an automated mechanism for oral cancer screening is required. The present work is an attempt towards developing an automated system for diagnosing oral lesions using deep learning techniques. An ensemble deep learning model that combines the benefits of Resnet-50 and VGG-16 has been developed. This model has been trained with an augmented dataset of oral lesion images. The model outperforms other popularly used deep learning models in performing the classification of oral images. An accuracy of 96.2%, 98.14% sensitivity and 94.23% specificity was achieved with the ensemble deep learning model.</p

An Ensemble Deep Neural Network Approach for Oral Cancer Screening

One of the ways to reduce oral cancer mortality rate is diagnosing oral lesions at initial stages to classify them as precancerous or normal lesions. During routine oral examination, oral lesions are normally screened manually. In a low resource setting area where there is lack of medical facilities and also medical expertise, an automated mechanism for oral cancer screening is required. The present work is an attempt towards developing an automated system for diagnosing oral lesions using deep learning techniques. An ensemble deep learning model that combines the benefits of Resnet-50 and VGG-16 has been developed. This model has been trained with an augmented dataset of oral lesion images. The model outperforms other popularly used deep learning models in performing the classification of oral images. An accuracy of 96.2%, 98.14% sensitivity and 94.23% specificity was achieved with the ensemble deep learning model