Search results for: blood detection

Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: Data mining, ensemble, radial basis function, support vector machine, accuracy.

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Authors: Fatima Zohra Mahi, Luca Varani

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We present an analytical model for the calculation of the sensitivity, the spectral current noise and the detective parameter for an optically illuminated In0.53Ga0.47As n+nn+ diode. The photocurrent due to the excess carrier is obtained by solving the continuity equation. Moreover, the current noise level is evaluated at room temperature and under a constant voltage applied between the diode terminals. The analytical calculation of the current noise in the n+nn+ structure is developed by considering the free carries fluctuations. The responsivity and the detection parameter are discussed as functions of the doping concentrations and the emitter layer thickness in one-dimensional homogeneous n+nn+ structure.

Keywords: Responsivity, detection parameter, photo-detectors, continuity equation, current noise.

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Authors: Roberto Sabatini, Alessandro Gardi, Mark A. Richardson

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The availability of powerful eye-safe laser sources and the recent advancements in electro-optical and mechanical beam-steering components have allowed laser-based Light Detection and Ranging (LIDAR) to become a promising technology for obstacle warning and avoidance in a variety of manned and unmanned aircraft applications. LIDAR outstanding angular resolution and accuracy characteristics are coupled to its good detection performance in a wide range of incidence angles and weather conditions, providing an ideal obstacle avoidance solution, which is especially attractive in low-level flying platforms such as helicopters and small-to-medium size Unmanned Aircraft (UA). The Laser Obstacle Avoidance Marconi (LOAM) system is one of such systems, which was jointly developed and tested by SELEX-ES and the Italian Air Force Research and Flight Test Centre. The system was originally conceived for military rotorcraft platforms and, in this paper, we briefly review the previous work and discuss in more details some of the key development activities required for integration of LOAM on UA platforms. The main hardware and software design features of this LOAM variant are presented, including a brief description of the system interfaces and sensor characteristics, together with the system performance models and data processing algorithms for obstacle detection, classification and avoidance. In particular, the paper focuses on the algorithm proposed for optimal avoidance trajectory generation in UA applications.

Keywords: LIDAR, Low-Level Flight, Nap-of-the-Earth Flight, Near Infra-Red, Obstacle Avoidance, Obstacle Detection, Obstacle Warning System, Sense and Avoid, Trajectory Optimisation, Unmanned Aircraft.

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Authors: Haibo Hu, Dan Yang, Chunxiao Ye, Chunlei Fu, Ren Li

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High quality requirements analysis is one of the most crucial activities to ensure the success of a software project, so that requirements verification for software system becomes more and more important in Requirements Engineering (RE) and it is one of the most helpful strategies for improving the quality of software system. Related works show that requirement elicitation and analysis can be facilitated by ontological approaches and semantic web technologies. In this paper, we proposed a hybrid method which aims to verify requirements with structural and formal semantics to detect interactions. The proposed method is twofold: one is for modeling requirements with the semantic web language OWL, to construct a semantic context; the other is a set of interaction detection rules which are derived from scenario-based analysis and represented with semantic web rule language (SWRL). SWRL based rules are working with rule engines like Jess to reason in semantic context for requirements thus to detect interactions. The benefits of the proposed method lie in three aspects: the method (i) provides systematic steps for modeling requirements with an ontological approach, (ii) offers synergy of requirements elicitation and domain engineering for knowledge sharing, and (3)the proposed rules can systematically assist in requirements interaction detection.

Keywords: Requirements Engineering, Semantic Web, OWL, Requirements Interaction Detection, SWRL.

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Authors: El-Sayed, Mona, Y., U. E. Mahrous

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A total of 60 male mule ducks and 60 male Muscovy ducks were allotted into three groups (n = 20) to estimate the effects of overfeeding (two and four meals) versus ad libitum feeding on productive performance traits, foie gras production, internal organs, and blood parameters.

The results show that force-feeding four meals significantly increased (P < 0.01) body weight, weight gain, and gain percentage compared to force-feeding two meals. Both force-feeding regimes (two or four meals) induced significantly higher body weight, weight gain, gain percentage, and absolute carcass weight than ad libitum feeding; however, carcass percentage was significantly higher in ad libitum feeding. Mule ducks had significantly higher weight gain and weight gain percentages than Muscovy ducks.

Feed consumption per kilogram of foie gras and per kilogram weight gain was lower for the four-meal than for the two-meal forced feeding regime. Force-feeding four meals induced significantly higher liver weight and percentage (488.96 ± 25.78g, 7.82 ± 0.40%) than force-feeding two meals (381.98 ± 13.60g, 6.42 ± 0.21%). Moreover, feed conversion was significantly higher under forced feeding than under ad libitum feeding (77.65 ± 3.41g, 1.72 ± 0.05%; P < 0.01).

Forced feeding (two or four meals) increased all organ weights (intestine, proventriculus, heart, spleen, and pancreas) over ad libitum feeding weights, except for the gizzard; however intestinal and abdominal fat values were higher for four-meal forced feeding than for two-meal forced feeding.

Overfeeding did not change blood parameters significantly compared to ad libitum feeding; however, four-meal forced feeding improved the quality of foie gras since it significantly increased the percentage of grade A foie gras (62.5%) at the expense of grades B (33.33%) and C (4.17%) compared with the two-meal forced feeding.

The mortality percentage among Muscovy ducks during the forced feeding period was 22.5%, compared to 0% in mule ducks. Liver weight was highly significantly correlated with life weight after overfeeding and certain blood plasma traits.

Keywords: Foie gras, overfeeding, ducks, productive performance.

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Authors: Elya S. Pepoyan, Astghik Z. Pepoyan

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The Zung self-depression scale and Beck Anxiety Inventory were used both to study the depression and anxiety levels of Armenian Crohn’s disease patients, and to reveal the relation between emotional status and placebo effect of these patients. On the other hand, the blood cell analyses and gut bacteria investigations were used to assess the placebo effect on ESR, and haemoglobin-s and leukocyte-s levels as well as gut commensal E. coli quantities of these patients. Despite of registered high levels of depression and anxiety, the high placebo effect on psychoemotional status for investigated patients during the investigations was described. On the other hand, no positive effect of placebo on measurements of ESP and hemoglobin-s levels of Crohn’s disease patients was revealed. The importance of use of psychotherapies for optimal outcomes during treatments of Crohn’s disease is discussed.

Keywords: Crohn's disease, emotional disorders, placebo, gut microflora and blood composition.

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Authors: H. Heidari-Bafroui, B. Ribeiro, A. Charbaji, C. Anagnostopoulos, M. Faghri

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In this paper, we report the development of a portable and inexpensive infrared lightbox for improving the detection limits of paper-based phosphate devices. Commercial paper-based devices utilize the molybdenum blue protocol to detect phosphate in the environment. Although these devices are easy to use and have a long shelf life, their main deficiency is their low sensitivity based on the qualitative results obtained via a color chart. To improve the results, we constructed a compact infrared lightbox that communicates wirelessly with a smartphone. The system measures the absorbance of radiation for the molybdenum blue reaction in the infrared region of the spectrum. It consists of a lightbox illuminated by four infrared light-emitting diodes, an infrared digital camera, a Raspberry Pi microcontroller, a mini-router, and an iPhone to control the microcontroller. An iPhone application was also developed to analyze images captured by the infrared camera in order to quantify phosphate concentrations. Additionally, the app connects to an online data center to present a highly scalable worldwide system for tracking and analyzing field measurements. In this study, the detection limits for two popular commercial devices were improved by a factor of 4 for the Quantofix devices (from 1.3 ppm using visible light to 300 ppb using infrared illumination) and a factor of 6 for the Indigo units (from 9.2 ppm to 1.4 ppm) with repeatability of less than or equal to 1.2% relative standard deviation (RSD). The system also provides more granular concentration information compared to the discrete color chart used by commercial devices and it can be easily adapted for use in other applications.

Keywords: Infrared lightbox, paper-based device, phosphate detection, smartphone colorimetric analyzer.

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Authors: Archana Panchapakesan Iyer, Taha Abdullah Kumosani

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Many high-risk pathogens that cause disease in humans are transmitted through various food items. Food-borne disease constitutes a major public health problem. Assessment of the quality and safety of foods is important in human health. Rapid and easy detection of pathogenic organisms will facilitate precautionary measures to maintain healthy food. The Polymerase Chain Reaction (PCR) is a handy tool for rapid detection of low numbers of bacteria. We have designed gene specific primers for most common food borne pathogens such as Staphylococci, Salmonella and E.coli. Bacteria were isolated from food samples of various food outlets and identified using gene specific PCRs. We identified Staphylococci, Salmonella and E.coli O157 using gene specific primers by rapid and direct PCR technique in various food samples. This study helps us in getting a complete picture of the various pathogens that threaten to cause and spread food borne diseases and it would also enable establishment of a routine procedure and methodology for rapid identification of food borne bacteria using the rapid technique of direct PCR. This study will also enable us to judge the efficiency of present food safety steps taken by food manufacturers and exporters.

Keywords: food borne pathogens, PCR, food safety, rapiddetection.

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Authors: JooHyung Oh, Dongwan Kang, Sekwon Kim, ChaeTae Im

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Currently, there has been a 3G mobile networks data traffic explosion due to the large increase in the number of smartphone users. Unlike a traditional wired infrastructure, 3G mobile networks have limited wireless resources and signaling procedures for complex wireless resource management. And mobile network security for various abnormal and malicious traffic technologies was not ready. So Malicious or potentially malicious traffic originating from mobile malware infected smart devices can cause serious problems to the 3G mobile networks, such as DoS and scanning attack in wired networks. This paper describes the DoS security threat in the 3G mobile network and proposes a detection technology.

Keywords: 3G, WCDMA, DoS, Security Threat

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Authors: Ehsan Yazdanpanah Moghadam, Muthukumaran Packirisamy

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In the present study, a design of the suspended polymeric microfluidic platform is introduced that is fabricated with three polymeric layers. Changing the microchannel plane to be perpendicular to microcantilever plane, drastically decreases moment of inertia in that direction. In addition, the platform is made of polymer (around five orders of magnitude less compared to silicon). It causes significant increase in the sensitivity of the cantilever deflection. Next, although the dimensions of this platform are constant, by misaligning the embedded microchannels laterally in the suspended microfluidic platform, the sensitivity can be highly increased. The investigation is studied on four fluids including water, seawater, milk, and blood for flow ranges from low rate of 5 to 70 µl/min to obtain the best design with the highest sensitivity. The best design in this study shows the sensitivity increases around 50% for water, seawater, milk, and blood at the flow rate of 70 µl/min by just misaligning the embedded microchannels in the suspended polymeric microfluidic platform.

Keywords: Microfluidic, biosensor, MEMS.

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Authors: Wadii Boulila, Imed Riadh Farah, Karim Saheb Ettabaa, Basel Solaiman, Henda Ben Ghezala

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This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.

Keywords: Knowledge discovery in satellite databases, knowledge fusion, data imperfection, data mining, spatiotemporal change detection.

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Authors: N. Tadrisi Parsa, A. R. Vali, R. Ghasemi

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Diabetes is a growing health problem in worldwide. Especially, the patients with Type 1 diabetes need strict glycemic control because they have deficiency of insulin production. This paper attempts to control blood glucose based on body mathematical body model. The Bergman minimal mathematical model is used to develop the nonlinear controller. A novel back-stepping based sliding mode control (B-SMC) strategy is proposed as a solution that guarantees practical tracking of a desired glucose concentration. In order to show the performance of the proposed design, it is compared with conventional linear and fuzzy controllers which have been done in previous researches. The numerical simulation result shows the advantages of sliding mode back stepping controller design to linear and fuzzy controllers.

Keywords: Back stepping, Bergman Model, Nonlinear control, Sliding mode control.

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Authors: Aswir Premadi, Mohammad Syuhaimi Ab. Rahman, Mohamad Najib Moh. Saupe, KasmiranJumari

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An optical fault monitoring in FTTH-PON using ACS is demonstrated. This device can achieve real-time fault monitoring for protection feeder fiber. In addition, the ACS can distinguish optical fiber fault from the transmission services to other customers in the FTTH-PON. It is essential to use a wavelength different from the triple-play services operating wavelengths for failure detection. ACS is using the operating wavelength 1625 nm for monitoring and failure detection control. Our solution works on a standard local area network (LAN) using a specially designed hardware interfaced with a microcontroller integrated Ethernet.

Keywords: ACS, monitoring tool, FTTH-PON.

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Authors: Sang-Youl Lee

Abstract:

This study deals with an advanced numerical techniques to detect tensile forces in cable-stayed structures. The proposed method allows us not only to avoid the trap of minimum at initial searching stage but also to find their final solutions in better numerical efficiency. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the cable model modeled using the finite element method. The results indicate that the proposed method is computationally efficient in characterizing the tensile force variation for cable-stayed structures.

Keywords: Tensile force detection, cable-stayed structures, hybrid system identification (h-SI), dynamic response.

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Authors: Saimir Heta, Ilma Robo, Nevila Alliu, Tea Meta

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Background: Plasma levels of inflammatory markers are the expression of the infectious wastes of existing periodontitis, as well as of existing inflammation everywhere in the body. Materials and Methods: The study consists of the clinical part of the measurement of inflammatory markers of 23 patients diagnosed with chronic periodontitis and the recording of parental periodontal parameters of patient periodontal status: hemorrhage index and probe values, before and 7-10 days after non-surgical periodontal treatment. Results: The level of fibrinogen drops according to the categorization of disease progression, active and passive, with the biggest % (18%-30%) at the fluctuation 10-20 mg/d. Fluctuations in fibrinogen level according to the age of patients in the range 0-10 mg/dL under 40 years and over 40 years was 13%-26%, in the range 10-20 mg/dL was 26%-22%, in the 20-40 mg/dL was 9%-4%. Conclusions: Non-surgical periodontal treatment significantly reduces the level of non-inflammatory markers in the blood. Oral health significantly reduces the potential source for periodontal bacteria, with the potential of promoting thromboembolism, through interaction between thrombocytes.

Keywords: Chronic periodontitis, atherosclerosis, risk factor, inflammatory markers.

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Authors: Yang Weiping, Zhang Zhilong, Li Jicheng, Chen Zengping, He Jun

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The background estimation approach using a small window median filter is presented on the bases of analyzing IR point target, noise and clutter model. After simplifying the two-dimensional filter, a simple method of adopting one-dimensional median filter is illustrated to make estimations of background according to the characteristics of IR scanning system. The adaptive threshold is used to segment canceled image in the background. Experimental results show that the algorithm achieved good performance and satisfy the requirement of big size image-s real-time processing.

Keywords: Point target, background estimation, median filter, adaptive threshold, target detection.

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: Cyber-security, Intrusion Detection Systems, Temporal Graph Network, Anomaly Detection.

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Authors: Zaw Tun, Aung Htein Maw

Abstract:

The nature of wireless ad hoc and sensor networks make them very attractive to attackers. One of the most popular and serious attacks in wireless ad hoc networks is wormhole attack and most proposed protocols to defend against this attack used positioning devices, synchronized clocks, or directional antennas. This paper analyzes the nature of wormhole attack and existing methods of defending mechanism and then proposes round trip time (RTT) and neighbor numbers based wormhole detection mechanism. The consideration of proposed mechanism is the RTT between two successive nodes and those nodes- neighbor number which is needed to compare those values of other successive nodes. The identification of wormhole attacks is based on the two faces. The first consideration is that the transmission time between two wormhole attack affected nodes is considerable higher than that between two normal neighbor nodes. The second detection mechanism is based on the fact that by introducing new links into the network, the adversary increases the number of neighbors of the nodes within its radius. This system does not require any specific hardware, has good performance and little overhead and also does not consume extra energy. The proposed system is designed in ad hoc on-demand distance vector (AODV) routing protocol and analysis and simulations of the proposed system are performed in network simulator (ns-2).

Keywords: AODV, Wormhole attacks, Wireless ad hoc andsensor networks

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Authors: Alaa Al Hawari, Mohammad Khader, Tarek Zayed, Osama Moselhi

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Ground Penetrating Radar (GPR) is one of the most effective electromagnetic techniques for non-destructive non-invasive subsurface features investigation. Water leak from pipelines is the most common undesirable reason of potable water losses. Rapid detection of such losses is going to enhance the use of the Water Distribution Networks (WDN) and decrease threatens associated with water mains leaks. In this study, GPR approach was developed to detect leaks by implementing an appropriate imaging analyzing strategy based on image refinement, reflection polarity and reflection amplitude that would ease the process of interpreting the collected raw radargram image.

Keywords: Water Networks, Leakage, Water pipelines, Ground Penetrating Radar.

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Authors: M. F. Yaqub, I. Gondal, J. Kamruzzaman

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Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques.

Keywords: Envelope Detection, Wavelet Transform, Bearing Faults, Machine Health Monitoring.

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Authors: F.Katlane, M.S.Naceur, M.A.Loghmari

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Automatic methods of detecting changes through satellite imaging are the object of growing interest, especially beca²use of numerous applications linked to analysis of the Earth’s surface or the environment (monitoring vegetation, updating maps, risk management, etc...). This work implemented spatial analysis techniques by using images with different spatial and spectral resolutions on different dates. The work was based on the principle of control charts in order to set the upper and lower limits beyond which a change would be noted. Later, the a contrario approach was used. This was done by testing different thresholds for which the difference calculated between two pixels was significant. Finally, labeled images were considered, giving a particularly low difference which meant that the number of “false changes” could be estimated according to a given limit.

Keywords: multi-scale, a contrario approach, significantthresholds, change detection.

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Authors: S. Umarani, D. Sharmila

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A Distributed Denial of Service (DDoS) attack is a major threat to cyber security. It originates from the network layer or the application layer of compromised/attacker systems which are connected to the network. The impact of this attack ranges from the simple inconvenience to use a particular service to causing major failures at the targeted server. When there is heavy traffic flow to a target server, it is necessary to classify the legitimate access and attacks. In this paper, a novel method is proposed to detect DDoS attacks from the traces of traffic flow. An access matrix is created from the traces. As the access matrix is multi dimensional, Principle Component Analysis (PCA) is used to reduce the attributes used for detection. Two classifiers Naive Bayes and K-Nearest neighborhood are used to classify the traffic as normal or abnormal. The performance of the classifier with PCA selected attributes and actual attributes of access matrix is compared by the detection rate and False Positive Rate (FPR).

Keywords: Distributed Denial of Service (DDoS) attack, Application layer DDoS, DDoS Detection, K- Nearest neighborhood classifier, Naive Bayes Classifier, Principle Component Analysis.

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Authors: Nur Ilyani Ramli, Mansour Youseffi, Peter Widdop

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This paper presents a low cost design of heart beat monitoring device using reflectance mode PhotoPlethysmography (PPG). PPG is known for its simple construction, ease of use and cost effectiveness and can provide information about the changes in cardiac activity as well as aid in earlier non-invasive diagnostics. The proposed device is divided into three phases. First is the detection of pulses through the fingertip. The signal is then passed to the signal processing unit for the purpose of amplification, filtering and digitizing. Finally the heart rate is calculated and displayed on the computer using parallel port interface. The paper is concluded with prototyping of the device followed by verification procedure of the heartbeat signal obtained in laboratory setting.

Keywords: Reflectance mode PPG, Heart beat detection, Circuitdesign, PCB design

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Authors: Zulkiflee M., Robiah Y., Nur Azman Abu, Shahrin S.

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Malware is software which was invented and meant for doing harms on computers. Malware is becoming a significant threat in computer network nowadays. Malware attack is not just only involving financial lost but it can also cause fatal errors which may cost lives in some cases. As new Internet Protocol version 6 (IPv6) emerged, many people believe this protocol could solve most malware propagation issues due to its broader addressing scheme. As IPv6 is still new compares to native IPv4, some transition mechanisms have been introduced to promote smoother migration. Unfortunately, these transition mechanisms allow some malwares to propagate its attack from IPv4 to IPv6 network environment. In this paper, a proof of concept shall be presented in order to show that some existing IPv4 malware detection technique need to be improvised in order to detect malware attack in dual-stack network more efficiently. A testbed of dual-stack network environment has been deployed and some genuine malware have been released to observe their behaviors. The results between these different scenarios will be analyzed and discussed further in term of their behaviors and propagation methods. The results show that malware behave differently on IPv6 from the IPv4 network protocol on the dual-stack network environment. A new detection technique is called for in order to cater this problem in the near future.

Keywords: Dual-Stack, Malware, Worm, IPv6;IDS

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Authors: Maad Kamal Al-Anni

Abstract:

This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Mobile ad hoc network, MANET, intrusion detection system, back propagation algorithm, neural networks, traffic table, multilayer perceptron, feed-forward back-propagation, network simulator 2.

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Authors: Nebi Gedik

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Regardless of age or gender, cardiovascular illnesses are a serious health concern because of things like poor eating habits, stress, a sedentary lifestyle, hard work schedules, alcohol use, and weight. It tends to happen suddenly and has a high rate of recurrence. Machine learning models can be implemented to assist healthcare systems in the accurate detection and diagnosis of cardiovascular disease (CVD) in patients. Improved heart failure prediction is one of the primary goals of researchers using the heart disease dataset. The purpose of this study is to identify the feature or features that offer the best classification prediction for CVD detection. The support vector machine classifier is used to compare each feature's performance. It has been determined which feature produces the best results.

Keywords: Cardiovascular disease, feature extraction, supervised learning, support vector machine.

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Authors: Sauravjyoti Sarmah, Rosy Das, Dhruba Kr. Bhattacharyya

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Clustering algorithms help to understand the hidden information present in datasets. A dataset may contain intrinsic and nested clusters, the detection of which is of utmost importance. This paper presents a Distributed Grid-based Density Clustering algorithm capable of identifying arbitrary shaped embedded clusters as well as multi-density clusters over large spatial datasets. For handling massive datasets, we implemented our method using a 'sharednothing' architecture where multiple computers are interconnected over a network. Experimental results are reported to establish the superiority of the technique in terms of scale-up, speedup as well as cluster quality.

Keywords: Clustering, Density-based, Grid-based, Adaptive Grid.

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Authors: Y. Bentoutou

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On-board Error Detection and Correction (EDAC) devices aim to secure data transmitted between the central processing unit (CPU) of a satellite onboard computer and its local memory. This paper presents a comparison of the performance of four low complexity EDAC techniques for application in Random Access Memories (RAMs) on-board small satellites. The performance of a newly proposed EDAC architecture is measured and compared with three different EDAC strategies, using the same FPGA technology. A statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard Alsat-1 is given for a period of 8 years

Keywords: Error Detection and Correction; On-board computer; small satellite missions

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Authors: Thuy Thi Nguyen, Nguyen Dang Binh, Horst Bischof

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In this work, we present a novel active learning approach for learning a visual object detection system. Our system is composed of an active learning mechanism as wrapper around a sub-algorithm which implement an online boosting-based learning object detector. In the core is a combination of a bootstrap procedure and a semi automatic learning process based on the online boosting procedure. The idea is to exploit the availability of classifier during learning to automatically label training samples and increasingly improves the classifier. This addresses the issue of reducing labeling effort meanwhile obtain better performance. In addition, we propose a verification process for further improvement of the classifier. The idea is to allow re-update on seen data during learning for stabilizing the detector. The main contribution of this empirical study is a demonstration that active learning based on an online boosting approach trained in this manner can achieve results comparable or even outperform a framework trained in conventional manner using much more labeling effort. Empirical experiments on challenging data set for specific object deteciton problems show the effectiveness of our approach.

Keywords: Computer vision, object detection, online boosting, active learning, labeling complexity.

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Authors: Chiang-Ho Cheng, An-Shik Yang, Hong-Yih Cheng

Abstract:

This paper applies the MEMS technology to design and fabricate a micro-bubble generator by a piezoelectric actuator. Coupled with a nickel nozzle plate, an annular piezoelectric ceramic was utilized as the primary structure of the generator. In operations, the piezoelectric element deforms transversely under an electric field applied across the thickness of the generator. The surface of the nozzle plate can expand or contract because of the induction of radial strain, resulting in the whole structure to bend, and successively transport oxygen micro-bubbles into the blood flow for enhancing the oxygen content in blood. In the tests, a high magnification microscope and a high speed CCD camera were employed to photograph the time evolution of meniscus shape of gaseous bubbles dispensed from the micro-bubble generator for flow visualization. This investigation thus explored the bubble formation process including the influences of inlet gas pressure along with driving voltage and resonance frequency on the formed bubble extent.

Keywords: Micro-bubble, nozzle, oxygenator, piezoelectric.

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