Search results for: small object detection

Authors: Shoujia Fang, Guoqing Ding, Xin Chen

Abstract:

The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

Procedia PDF Downloads 218

Authors: K. A.Tharanga, K. H. H. Damayanthi

Abstract:

Perception is an epistemological concept discussed in Philosophy. Perception, in other word, vision, is one of the ways that human beings get empirical knowledge after five senses. However, we face innumerable problems when achieving knowledge from perception, and therefore the knowledge gained through perception is uncertain. what we see in the external world is not real. These are the major issues that we face when receiving knowledge through perception. Sometimes there is no physical existence of what we really see. In such cases, the perception is relative. The following frames will be taken into consideration when perception is analyzed illusions and delusions, the figure of a physical object, appearance and the reality of a physical object, time factor, and colour of a physical object. seeing and knowing become vary according to the above conceptual frames. We cannot come to a proper conclusion of what we see in the empirical world. Because the things that we see are not really there. Hence the scientific knowledge which is gained from observation is doubtful. All the factors discussed in science remain in the physical world. There is a leap from ones existence to the existence of a world outside his/her mind. Indeed, one can suppose that what he/she takes to be real is just a massive deception. However, depending on the above facts, if someone begins to doubt about the whole world, it is unavoidable to become his/her view a scepticism or nihilism. This is a certain reality.

Keywords: empirical, perception, sceptisism, nihilism

Procedia PDF Downloads 133

Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

Abstract:

Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

Procedia PDF Downloads 124

Authors: A. Harras, A. Tsuji, K. Terada

Abstract:

Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.

Keywords: automatic detection, tracking, pedestrians, counting

Procedia PDF Downloads 249

Authors: Gigih Priyandoko, Mohd Fairusham Ghazali, Tan Siew Fun

Abstract:

This paper discusses about the defect detection of plastic pipe by using nonlinear acoustic wave modulation method. It is a sensitive method for damage detection and it is based on the propagation of high frequency acoustic waves in plastic pipe with low frequency excitation. The plastic pipe is excited simultaneously with a slow amplitude modulated vibration pumping wave and a constant amplitude probing wave. The frequency of both the excitation signals coincides with the resonances of the plastic pipe. A PVP pipe is used as the specimen as it is commonly used for the conveyance of liquid in many fields. The results obtained are being observed and the difference between uncracked specimen and cracked specimen can be distinguished clearly.

Keywords: plastic pipe, defect detection, nonlinear acoustic modulation, excitation

Procedia PDF Downloads 444

Authors: Mrinal Kanti Bhowmik, Kakali Das Jr., Barin Kumar De, Debotosh Bhattacharjee

Abstract:

Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection.

Keywords: fractal, tumor, thermography, mammography

Procedia PDF Downloads 377

Authors: Awais Naeem, Osama Shakeel, Faizan Ullah, Abdul Wahid Anwer

Abstract:

Introduction: To study the clinic-pathological factors, diagnostic factors and survival of extra-pulmonary small cell carcinoma. Methodology: From 1995 to 2017 all patients with a diagnosis of extra-pulmonary small cell carcinoma were included in the study. Demographic variables and clinic-pathological factors were collected. Management of disease was recorded. Short and long term oncological outcomes were recorded. All data was entered and analyzed in SPSS version 21. Results: A total of 14 patients were included in the study. Median age was 53.42 +/- 16.1 years. There were 5 male and 9 female patients. Most common presentation was dysphagia in 16 patient among esophageal small cell carcinoma and while other patient had pain in abdomen. Mean duration of symptoms was 4.23+/-2.91 months .Most common site is esophagus (n=6) followed by gall bladder(n=3). Almost all of the patients received chemo-radiotherapy. Majority of the patient presented with extensive disease. Five patients (35.7%) died during the follow up period, two (14.3%) were alive and rest of the patients were lost to follow up. Mean follow up period was 22.92 months and median follow up was 15 months. Conclusion: Extra-pulmonary small cell carcinoma is rare and needs to be managed aggressively. All patients should be treated with both systemic and local therapies.

Keywords: small cell carcinoma of esophagus, extrapulmonary small cell carcinoma, small cell carcinoma of gall bladder, small cell carcinoma of rectum, small cell carcinoma of stomach

Procedia PDF Downloads 151

Authors: Janusz Getta, Zhaoxi Pan

Abstract:

Performance-based quality evaluation of database conceptual schemas is an important aspect of database design process. It is evident that different conceptual schemas provide different logical schemas and performance of user applications strongly depends on logical and physical database structures. This work presents the entire process of performance-based quality evaluation of conceptual schemas. First, we show format. Then, the paper proposes a new specification of object algebra for representation of conceptual level database applications. Transformation of conceptual schemas and expression of object algebra into implementation schema and implementation in a particular database system allows for precise estimation of the processing costs of database applications and as a consequence for precise evaluation of performance-based quality of conceptual schemas. Then we describe an experiment as a proof of concept for the evaluation procedure presented in the paper.

Keywords: conceptual schema, implementation schema, logical schema, object algebra, performance evaluation, query processing

Procedia PDF Downloads 286

Authors: Michaela Vašková

Abstract:

With the increasing dependence of countries on the critical infrastructure, it increases their vulnerability. Big threat is primarily in the human factor (personnel of the critical infrastructure) and in terrorist attacks. It emphasizes the development of methodology for searching of weak points and their subsequent elimination. This article discusses methods for the analysis of safety in the objects of critical infrastructure. It also contains proposal for methodology for training employees of security services in the objects of the critical infrastructure and developing scenarios of attacks on selected objects of the critical infrastructure.

Keywords: critical infrastructure, object of critical infrastructure, protection, safety, security, security audit

Procedia PDF Downloads 338

Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

Abstract:

Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

Procedia PDF Downloads 349

Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

Abstract:

Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

Procedia PDF Downloads 102

Authors: Alireza Pour Ebrahimi, Vahid Abasi

Abstract:

Recently by the extension of internet usage, Research on the intrusion detection system takes a significant importance. Many of improvement systems prevent internal and external network attacks by providing security through firewalls and antivirus. In recently years, intrusion detection systems gradually turn from host-based systems and depend on O.S to the distributed systems which are running on multiple O.S. In this work, by considering the diversity of computer networks whit respect to structure, architecture, resource, services, users and also security goals requirement a fully distributed collaborative intrusion detection system based on peer to peer architecture is suggested. in this platform each partner device (matched device) considered as a peer-to-peer network. All transmitted information to network are visible only for device that use security scanning of a source. Experimental results show that the distributed architecture is significantly upgradeable in respect to centralized approach.

Keywords: network, intrusion detection system, peer to peer, internal and external network

Procedia PDF Downloads 538

Authors: Nataša Todorović, Jovana Nikolov, Ivana Stojković, Milan Vraneš, Jovana Panić, Slobodan Gadžurić

Abstract:

The detection of ²¹⁰Pb levels in aquatic environments evokes interest in various scientific studies. Its precise determination is important not only for the radiological assessment of drinking waters but also ²¹⁰Pb, and ²¹⁰Po distribution in the marine environment are significant for the assessment of the removal rates of particles from the ocean and particle fluxes during transport along the coast, as well as particulate organic carbon export in the upper ocean. Measurement techniques for ²¹⁰Pb determination, gamma spectrometry, alpha spectrometry, or liquid scintillation counting (LSC) are either time-consuming or demand expensive equipment or complicated chemical pre-treatments. However, one other possibility is to measure ²¹⁰Pb on an LS counter if it is in equilibrium with its progeny ²¹⁰Bi - through the Cherenkov counting method. It is unaffected by the chemical quenching and assumes easy sample preparation but has the drawback of lower counting efficiencies than standard LSC methods, typically from 10% up to 20%. The aim of the presented research in this paper is to investigate the possible increment of detection efficiency of Cherenkov counting during ²¹⁰Pb/²¹⁰Bi detection on an LS counter Quantulus 1220. Considering naturally low levels of ²¹⁰Pb in aqueous samples, the addition of ionic liquids to the counting vials with the analysed samples has the benefit of detection limit’s decrement during ²¹⁰Pb quantification. Our results demonstrated that ionic liquid, 1-butyl-3-methylimidazolium salicylate, is more efficient in Cherenkov counting efficiency increment than the previously explored 2-hydroxypropan-1-amminium salicylate. Consequently, the impact of a few other ionic liquids that were synthesized with the same cation group (1-butyl-3-methylimidazolium benzoate, 1-butyl-3-methylimidazolium 3-hydroxybenzoate, and 1-butyl-3-methylimidazolium 4-hydroxybenzoate) was explored in order to test their potential influence on Cherenkov counting efficiency. It was confirmed that, among the explored ones, only ionic liquids in the form of salicylates exhibit a wavelength shifting effect. Namely, the addition of small amounts (around 0.8 g) of 1-butyl-3-methylimidazolium salicylate increases the detection efficiency from 16% to >70%, consequently reducing the detection threshold by more than four times. Moreover, the addition of ionic liquids could find application in the quantification of other radionuclides besides ²¹⁰Pb/²¹⁰Bi via Cherenkov counting method.

Keywords: liquid scintillation counting, ionic liquids, Cherenkov counting, ²¹⁰PB/²¹⁰BI in water

Procedia PDF Downloads 96

Authors: V. Verma, Syed Riyaz-ul-Hassan

Abstract:

Staphylococcus aureus is one of the most commonly found pathogenic bacteria and is hard to eliminate from the human environment. It is responsible for many nosocomial infections, besides being the main causative agent of food intoxication by virtue of its variety of enterotoxins. Routine detection of S. aureus in food is usually carried out by traditional methods based on morphological and biochemical characterization. These methods are time-consuming and tedious. In addition, misclassifications with automated susceptibility testing systems or commercially available latex agglutination kits have been reported by several workers. Consequently, there is a need for methods to specifically discriminate S. aureus from other staphylococci as quickly as possible. Data on protocols developed using molecular means like PCR technology will be presented for rapid and specific detection of this pathogen in food, clinical and environmental samples, especially milk.

Keywords: food Pathogens, PCR technology, rapid and specific detection, staphylococcus aureus

Procedia PDF Downloads 511

Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

Abstract:

Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

Procedia PDF Downloads 411

Authors: Li Li, Li-Ru Xia, He-Ye Wang, Xiao-Dong Bi

Abstract:

In this paper, we presented a highly sensitive immune-affinity monolithic array for detection of α-fetoprotein (AFP) and carcinoembryonic antigen (CEA). Firstly, the epoxy functionalized monolith arrays were fabricated using UV initiated copolymerization method. Scanning electron microscopy (SEM) image showed that the poly(BABEA-co-GMA) monolith exhibited a well-controlled skeletal and well-distributed porous structure. Then, AFP and CEA immune-affinity monolithic arrays were prepared by immobilization of AFP and CEA antibodies on epoxy functionalized monolith arrays. With a non-competitive immune response format, the presented AFP and CEA immune-affinity arrays were demonstrated as an inexpensive, flexible, homogeneous and stable array for detection of AFP and CEA.

Keywords: chemiluminescent detection, immune-affinity, monolithic copolymer array, UV-initiated copolymerization

Procedia PDF Downloads 331

Authors: Muhammad Ameer Nawaz Akram

Abstract:

The 21st century has been revealed that many individuals around the world are living in urban settlements than in rural zones. The evolution of numerous cities in emerging and newly developed countries is accompanied by the rise of slums. The precise definition of a slum varies countries to countries, but the universal harmony is that slums are dilapidated settlements facing severe poverty and have lacked access to sanitation, water, electricity, good living styles, and land tenure. The slum settlements always vary in unique patterns within and among the countries and cities. The core objective of this study is the spatial identification and classification of slums in Gujrat city Pakistan from very high-resolution GeoEye-1 (0.41m) satellite imagery. Slums were first identified using GPS for sample site identification and ground-truthing; through this process, 425 slums were identified. Then Object-Oriented Analysis (OOA) was applied to classify slums on digital image. Spatial analysis softwares, e.g., ArcGIS 10.3, Erdas Imagine 9.3, and Envi 5.1, were used for processing data and performing the analysis. Results show that OOA provides up to 90% accuracy for the identification of slums. Jalal Cheema and Allah Ho colonies are severely affected by slum settlements. The ratio of criminal activities is also higher here than in other areas. Slums are increasing with the passage of time in urban areas, and they will be like a hazardous problem in coming future. So now, the executive bodies need to make effective policies and move towards the amelioration process of the city.

Keywords: slums, GPS, satellite imagery, object oriented analysis, zonal change detection

Procedia PDF Downloads 125

Authors: Ahmad Zaini, F. Muhammad Reza Hadafi, Surya Sumpeno, Muhtadin, Mochamad Hariadi

Abstract:

The lack of information about museum’s collection reduces the number of visits of museum. Museum’s revitalization is an urgent activity to increase the number of visits. The research's roadmap is building a web-based application that visualizes museum in the virtual form including museum's statue reconstruction in the form of 3D. This paper describes implementation of three-dimensional model reconstruction method based on light-strip pattern on the museum statue using 3D scanner. Noise removal, alignment, meshing and refinement model's processes is implemented to get a better 3D object reconstruction. Model’s texture derives from surface texture mapping between object's images with reconstructed 3D model. Accuracy test of dimension of the model is measured by calculating relative error of virtual model dimension compared against the original object. The result is realistic three-dimensional model textured with relative error around 4.3% to 5.8%.

Keywords: 3D reconstruction, light pattern structure, texture mapping, museum

Procedia PDF Downloads 456

Authors: Weihao Wang, Zhulin Zong

Abstract:

Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

Procedia PDF Downloads 72

Authors: Tomáš Bublík, Miroslav Virius

Abstract:

This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and sub graph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm.

Keywords: AST, Java, tree matching, scripthon source code recognition

Procedia PDF Downloads 422

Authors: Qiang Zhang, Xiaojian Hu

Abstract:

In multi-vehicle tracking, based on feature selection, the tracking system efficiently tracks vehicles in a video with minimal error in combination with color attribution, which focuses on presenting a simple and fast, yet accurate and robust solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the intersection scenario. In this study, a real-time tracking system is proposed for multi-vehicle tracking in the intersection scene. Considering the complexity and application feasibility of the algorithm, in the object detection step, the detection result provided by virtual loops were post-processed and then used as the input for the tracker. For the tracker, lightweight methods were designed to extract and select features and incorporate them into the adaptive color tracking (ACT) framework. And the approbatory online feature selection algorithms are integrated on the mature ACT system with good compatibility. The proposed feature selection methods and multi-vehicle tracking method are evaluated on KITTI datasets and show efficient vehicle tracking performance when compared to the other state-of-the-art approaches in the same category. And the system performs excellently on the video sequences recorded at the intersection. Furthermore, the presented vehicle tracking system is suitable for surveillance applications.

Keywords: real-time, multi-vehicle tracking, feature selection, color attribution

Procedia PDF Downloads 157

Authors: M. Okeke, A. Blyth

Abstract:

Industrial Control Systems (ICS) such as Supervisory Control And Data Acquisition (SCADA) can be seen in many different critical infrastructures, from nuclear management to utility, medical equipment, power, waste and engine management on ships and planes. The role SCADA plays in critical infrastructure has resulted in a call to secure them. Many lives depend on it for daily activities and the attack vectors are becoming more sophisticated. Hence, the security of ICS is vital as malfunction of it might result in huge risk. This paper describes how the application of Prey Predator (PP) approach in flocks of birds could enhance the detection of malicious activities on ICS. The PP approach explains how these animals in groups or flocks detect predators by following some simple rules. They are not necessarily very intelligent animals but their approach in solving complex issues such as detection through corporation, coordination and communication worth emulating. This paper will emulate flocking behavior seen in birds in detecting predators. The PP approach will adopt six nearest bird approach in detecting any predator. Their local and global bests are based on the individual detection as well as group detection. The PP algorithm was designed following MapReduce methodology that follows a Split Detection Convergence (SDC) approach.

Keywords: artificial life, industrial control system (ICS), IDS, prey predator (PP), SCADA, SDC

Procedia PDF Downloads 296

Authors: Fathi M. O. Hamed, Salma F. Elkofhaifee

Abstract:

The aim of this work is to detect geometrical shape objects in an image. In this paper, the object is considered to be as a circle shape. The identification requires find three characteristics, which are number, size, and location of the object. To achieve the goal of this work, this paper presents an algorithm that combines from some of statistical approaches and image analysis techniques. This algorithm has been implemented to arrive at the major objectives in this paper. The algorithm has been evaluated by using simulated data, and yields good results, and then it has been applied to real data.

Keywords: image processing, median filter, projection, scale-space, segmentation, threshold

Procedia PDF Downloads 424

Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

Abstract:

Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

Procedia PDF Downloads 507

Authors: Muhammad Hassan Khalil, Xu Jiadong

Abstract:

Detection of malignant tumor inside the breast of women is a challenging field for the researchers. MWI (Microwave imaging) for breast cancer diagnosis has been of interest for last two decades, newly it suggested for finding cancerous tissues of women breast. A simple and basic idea of the mathematical modeling is used throughout this paper for imaging of malignant tumor. In this paper, the authors explained inverse scattering method in the microwave imaging and also present some simulation results.

Keywords: breast cancer detection, microwave imaging, tomography, tumor

Procedia PDF Downloads 404

Authors: Nimisha Elsa Koshy, Varun P. Gopi, V. I. Thajudin Ahamed

Abstract:

The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space.

Keywords: contourlet transform, log gabor filter, ulcer, wireless capsule endoscopy

Procedia PDF Downloads 534

Authors: Prikhil Agrawal, N. Priyanka

Abstract:

With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool.

Keywords: genetic algorithm (GA), intrusion detection system (IDS), dataset, network security

Procedia PDF Downloads 288

Authors: Ahlem Abid, Farah Jemili

Abstract:

Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

Procedia PDF Downloads 140

Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

Abstract:

In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

Procedia PDF Downloads 375

Authors: Amer Dawoud, Jesy Motchaalangaram, Arati Biswakarma, Wujan Mio, Karl Wallace

Abstract:

The development of an embedded miniaturized drone-based system for remote detection of Chemical Warfare Agents (CWA) is proposed. The paper focuses on the software/hardware system design of the electrochemical Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) signal processing for future deployment on drones. The paper summarizes the progress made towards hardware and electrochemical signal processing for signature detection of CWA. Also, the miniature potentiostat signal is validated by comparing it with the high-end lab potentiostat signal.

Keywords: drone-based, remote detection chemical warfare agents, miniaturized, potentiostat

Procedia PDF Downloads 127