Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

Background Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

Door Detection Based on Geometrical Features and Harris Corner

Door is a very significant element as it enables a person to enter a house or room. Though identifying doorway is an easy task for a regular person, for robots or visually impaired people it is a challenging task. To overcome this challenge, we have proposed a door detection method. Our proposed method is based on Prewitt edge detection method and Harris corner detector. Here, we are using a number of predefined rules to detect the doorframe correctly. To establish the robustness of our proposed method, we have formed a substantial dataset of scene images that are captured in various unfamiliar environments. Our experimental results validate that our proposed method is robust against changes in viewpoint, shapes, occlusions, illumination, colors, sizes, orientations, and textures of the door. The experimental results show that our proposed method reaches 87.45% accuracy as well as achieves lower false positive rate and lower computational time.

Detecting Vulnerability in Source Code Using CNN and LSTM Network

Vulnerabilities can have very serious consequences for information security, with huge implications for economic, social, and even national security. Automated vulnerability detection has always been a keen topic for researchers. From traditional manual vulnerability mining to static detection and dynamic detection, all rely on human experts to define features. The rapid development of machine learning and deep learning has alleviated the tedious task of manually defining features by human experts while reducing the lack of objectivity caused by human subjective awareness. However, we still need to find an objective characterization method to define the features of vulnerabilities. Therefore, we use code metrics for code characterization, which are sequences of metrics that represent code. To use code metrics for vulnerability detection, we propose VulnExplore, a deep learning-based vulnerability detection model that uses a composite neural network of CNN + LSTM for feature extraction and learning of code metrics. Experimental results show that VulnExplore has a lower false positive rate, a lower miss rate, and a better accuracy rate compared to other deep learning-based vulnerability detection models.

Data Leak Identification in Social Networks using K Means Clustering & Tabu K Means Clustering

The prevention of leakage of data has been defined as a process or solution which identifies data that is confidential, tracks the data in a way in which it moves in and out of its enterprise to prevent any unauthorized data disclosure in an intentional or an unintentional manner. As data that is confidential is able to reside on various computing devices and move through several network access points or different types of social networks such as emails. Leakage of emails has been defined as if the email either deliberately or accidentally goes to an addressee to whom it should not be addressed. Data Leak Prevention (DLP) is the technique or product that tries mitigating threats to data leaks. In this work, the technique of clustering will be combined with the frequency of the term or the inverse document frequency in order to identify the right centroids for analysing the various emails that are communicated among members of an organization. Every member will fit in to various topic clusters and one such topic cluster can also comprise of several members in the organization who have not communicated with each other earlier. At the time when a new email is composed, every addressee will be categorized to be a potential leak recipient or one that is legal. Such classification was based on the emails sent among the sender and the receiver and also on their topic clusters. The work had investigated the technique of K-Means clustering and also proposed a Tabu - K-Means (TABU-KM) technique of clustering to identify points of optimal clustering. The proposed TABU-KM optimizes the K-Means clustering. Experimental results demonstrated that the proposed method achieves higher True Positive Rate (TPR) for known and unknown recipient and lower False Positive Rate (FPR) for known and unknown recipient

Implementation of maternal blood cell-free DNA testing in early screening for aneuploidies

Several externally blinded validation and implementation studies in the last 9 years have shown that it is now possible, through analysis of cell-free (cf) DNA in maternal blood, to effectively detect a high proportion of fetuses affected by trisomies 21, 18, and 13 at a much lower false-positive rate (FPR) than all other existing screening methods. This article is aimed at reviewing technical and clinical considerations for implementing cfDNA testing in routine practice, including methods of analysis, performance of the test, models for clinical implementation, and interpretation of results.

A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning

In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.

Detection of de novo copy number deletions from targeted sequencing of trios

De novo copy number deletions have been implicated in many diseases, but there is no formal method to date however that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms. We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability, and is available as open source software at github.com/JMF47/MDTS.

Automated linkage of patient records from disparate sources

We introduce an automated method of record linkage that has two key features, automated selection of match field interactions to include in the model for estimation and automated threshold determination for classifying record pairs to matches or non-matches. We applied our method to two real-world examples. The first example demonstrated results consistent with our earlier work: When data quality is adequate and the match field discriminating power is high, matching algorithms exhibit similar performance. The second example demonstrated that our method yields a lower false positive rate and higher positive predictive value than the Fellegi-Sunter model in the face of low data quality. When compared to the Fellegi-Sunter model, simulation studies suggest that our method exhibits better overall performance as indicated by higher area under the curve, and less biased estimates for both the match prevalence rate and the m- and u-probabilities over a range of data scenarios, especially when the match prevalence is extreme. Computationally, our method is as efficient as the Fellegi-Sunter model. We recommend this method in situations that an unsupervised linking algorithm is needed.