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Software and systems complexity can have a profound impact on information security. Such complexity is not only imposed by the imperative technical challenges of monitored heterogeneous and dynamic (IP and VLAN assignments) network infrastructures, but also through the advances in exploits and malware distribution mechanisms driven by the underground economics. In addition, operational business constraints (disruptions and consequences, manpower, and end-user satisfaction), increase the complexity of the problem domain... Copyright SANS Institut

Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a certain field (e.g., event detection for wireless sensor networks) or the main concern for which techniques have been specifically designed (e.g., fire detection using remote sensing techniques). These different approaches stem from different backgrounds of researchers dealing with fire, such as computer science, geography and earth observation, and fire safety. In this report we survey previous studies from three perspectives: (1) fire detection techniques for residential areas, (2) fire detection techniques for forests, and (3) contributions of sensor networks to early fire detection

Awareness of Breast Cancer and Its Early Detection Measures Among Female Students, Northern Ethiopia

Globally breast cancer is the most common of all cancers. Since risk reduction strategies cannot eliminate the majority of breast cancers, early detection remains the cornerstone of breast cancer control. This paper, therefore, attempts to assess the awareness of breast cancer and its early detection measures among female students in Mekelle University, Ethiopia. An institution based cross-sectional study was conducted on randomly selected female students. Multistage sampling technique was employed to select the participants. A pre-tested structured questionnaire was used. Data analysis was carried out using SPSS version 16. In this study, 760 students participated making a response rate of 96 percent. Respondents with good knowledge score for risk factors, early detections measures and warning signs of breast cancer were 1.4 percent, 3.6 percent and 22.1 percent respectively. The majority 477 (62.8 percent) of participants practiced self-breast examination. In conclusion the participants had poor knowledge of risk factors, early detection measures and early warning signs of breast cancer.Therefore, the Ministry of health of Ethiopia together with its stalk holders should strengthen providing IEC targeting women to increase their awareness about breast cancer and its early detection measure

Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller

Implantable, closed-loop devices for automated early detection and stimulation of epileptic seizures are promising treatment options for patients with severe epilepsy that cannot be treated with traditional means. Most approaches for early seizure detection in the literature are, however, not optimized for implementation on ultra-low power microcontrollers required for long-term implantation. In this paper we present a convolutional neural network for the early detection of seizures from intracranial EEG signals, designed specifically for this purpose. In addition, we investigate approximations to comply with hardware limits while preserving accuracy. We compare our approach to three previously proposed convolutional neural networks and a feature-based SVM classifier with respect to detection accuracy, latency and computational needs. Evaluation is based on a comprehensive database with long-term EEG recordings. The proposed method outperforms the other detectors with a median sensitivity of 0.96, false detection rate of 10.1 per hour and median detection delay of 3.7 seconds, while being the only approach suited to be realized on a low power microcontroller due to its parsimonious use of computational and memory resources.Comment: Accepted at IJCNN 201

Early diagnosis of Alzheimer's disease: update on combining genetic and brain-imaging measures.

Diagnosis of Alzheimer's disease is often missed or delayed in clinical practice; thus, methods to improve early detection would provide opportunities for early intervention, symptomatic treatment, and improved patient function. Emerging data suggest that the disease process begins years before clinical diagnostic confirmation. This paper reviews current research focusing on methods for more specific and sensitive early detection using measures of genetic risk for Alzheimer's disease and functional brain imaging. This approach aims to identify patients in a presymptomatic stage for early treatment to delay progressive cognitive decline and disease onset