Search results for: SQL injection attack classification

Authors: Carlos Gonzalez, Ernesto Linan

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We propose a methodology to conciliate two apparently contradictory processes in the development of secure obfuscated software and good software engineered software. Our methodology consists first in the system designers defining the type of security level required for the software. There are four types of attackers: casual attackers, hackers, institution attack, and government attack. Depending on the level of threat, the methodology we propose uses five or six teams to accomplish this task. One Software Engineer Team and one or two software Obfuscation Teams, and Compiler Team, these four teams will develop and compile the secure obfuscated software, a Code Breakers Team will test the results of the previous teams to see if the software is not broken at the required security level, and an Intrusion Analysis Team will analyze the results of the Code Breakers Team and propose solutions to the development teams to prevent the detected intrusions. We also present an analytical model to prove that our methodology is no only easier to use, but generates an economical way of producing secure obfuscated software.

Keywords: development methodology, obfuscated software, secure software development, software engineering

Procedia PDF Downloads 246

Authors: A. ElZawam, D. McLean, A. Tibary

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In alpaca, age at puberty is variable and the factors regulating the pattern of puberty and sexual maturation are a subject of controversy. Plasma testosterone level is often used as an indicator of sexual maturity. Our hypothesis is that hCG treatment will cause an increase in testosterone level that is correlated with animal age. The specific aim was to investigate the testicular tissue response to a single hCG injection by monitoring the serum testosterone concentration. Eighty four (n=84) males ranging in age from 6 to 60 months were used. Alpacas were grouped based on their ages into 15 groups. Each group had three to five male animals. Blood samples were collected from the jugular vein before treatment with hCG and 2 hours after intravenous administration of 3000 IU of hCG (Chorulon®). The serum was harvested and stored at -20ºC until the analysis. The effect of age on basal testosterone level and response to hCG treatment was evaluated by Analysis of Variance. As a result, basal serum testosterone concentrations were very low (<0.1ng/ml) until 9 months of age. Although basal serum testosterone concentrations increased steadily with age there was a significant variation amongst males within the same age group. Administration of 3000 IU of hCG, resulted in an average increase of 50% (P<0.05) in serum testosterone concentration after 2 hours. The percentage increase in serum testosterone in response to hCG stimulation varied from 51 to 81%. There was no correlation between the degree of response and age. However, the response to hCG injection presented two modes of increase depending on the age of animals. The first mode occurred at ages 9 to 14 months and the second mode was observed between 22 and 36 months. In conclusion, our results suggest that testicular growth and sensitivity to LH stimulation may be bimodal in the male alpaca with a rapid increase in growth and sensitivity between 9 and 14 months of age and a second phase of increased responsiveness after 21 months of ages.

Keywords: alpaca, testosterone, hCG, animal science

Procedia PDF Downloads 567

Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

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The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

Procedia PDF Downloads 162

Authors: Nadeen Eltoukhy, Sahar S. Sheta, Walaa Elnaggar, Karim Bakr

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Purpose: The aim of this study is to compare the clinical efficacy and side effects of oral versus intralesional propranolol treatment of infantile capillary hemangiomas in infants. Methods: The study enrolled 40 infants diagnosed with infantile capillary hemangiomas. Patients were divided into 2 groups: Group A (Non-invasive group) included 20 infants who received oral propranolol hydrochloride starting at a dose of 1 mg/kg/day BID, then increased to a max of 2 mg/kg/day BID gradually over 2 weeks for 3 months. Group B (Invasive group) included 20 infants who received intralesional propranolol injection at a dose of 1 mg/mL; the volume of the injected drug depended on the size of the lesion (0.2 mL injected per cm of lesion diameter), with a maximum volume of 1 mL for a lesion of 5 cm diameter under complete aseptic conditions in the operating theater. Results: At three months after initiating treatment, the circumferential size of the hemangioma showed a statistically significant decrease in both groups; in Group A from 3.66±2.89 cm to 1.56±1.26 cm with p-value <0.05 and in Group B from 2.99±2.73 cm to 1.32±1.18 cm with p-value <0.05. There is no statistically significant comparative difference between the two groups (p-value = 0.538 = insignificant). Regarding the complications of oral propranolol, one patient (5%) had bradycardia, and one patient (5%) had diarrhea. In the injection group, 20 patients (100%) had local edema, and one patient (5%) had a local infection. Conclusions: Both oral non-invasive and intralesional invasive propranolol are safely used to successfully treat and decrease the size of infantile hemangioma while showing no statistically comparative difference between both treatment techniques.

Keywords: hemangioma, oral beta blocker, intralesional beta blocker, infants

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

Procedia PDF Downloads 127

Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

Procedia PDF Downloads 365

Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

Procedia PDF Downloads 235

Authors: Feng-jie Xie, Jian-hong Yan

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Based on the complex network theory, we construct the air cargo network of the Belt and Road area, analyze its regional distribution and structural characteristics, measure the robustness of the network. The regional distribution results show that Southeast Asia and China have the most prominent development in the air cargo network of the Belt and Road area, Central Asia is the least developed. The structure characteristics found that the air cargo network has obvious small-world characteristics; the degree distribution has single-scale property; it shows a significant rich-club phenomenon simultaneously. The network robustness is measured by two attack strategies of degree and betweenness, but the betweenness of network nodes has a greater impact on network connectivity. And identified 24 key cities that have a large impact on the robustness of the network under the two attack strategies. Based on these results, recommendations are given to maintain the air cargo network connectivity in the Belt and Road area.

Keywords: air cargo, complex network, robustness, structure properties, The Belt and Road

Procedia PDF Downloads 191

Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

Procedia PDF Downloads 177

Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Khalid Ishola Bello

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Many religions and cultures believe in the existence of invisible beings who co-exist with man on earth. Muslims, for example, believe in malaikah (Angel) and jinn (demon), who have their source of creation from light and flame, respectively. Jinn, according to Islamic texts, possesses unique characteristics which give them an advantage over the man. Invisibility, transforming into or taking possession of another being are parts of advantages jinn have above man. Hence, jinn can attack man and truncate his well-being by causing malfunction of his physiological and psychological realms, which may go beyond physical health care. It is on this background that this paper aims to articulate the possibility of a demonic attack on human health and the care processes recommended by Islam to heal and restore well-being of the victim. Through analysis of the inductive, deductive, and historical approaches, the process of ruqyah (healing method based on recitation of the Qur’an) and hijamah (cupping) therapies shall be analyzed. The finding shows the efficacy of Islamic remedies to demonic possession, which usually complicates health challenges in the care of man. This alternative approach is therefore recommended for holistic health care since physical health care cannot fix spiritual health challenges.

Keywords: wellbeing, healthcare, demonic possession, cupping, jinn

Procedia PDF Downloads 62

Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

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Authors: Nikhil Yadav, Shubham Verma

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Gas lift is a common technique used to optimize oil production in wells. Prosper software is a powerful tool for modeling and optimizing gas lift systems in oil wells. This review paper examines the effectiveness of Prosper software in optimizing gas lift systems in oil-producing wells. The literature review identified several studies that demonstrated the use of Prosper software to adjust injection rate, depth, and valve characteristics to optimize gas lift system performance. The results showed that Prosper software can significantly improve production rates and reduce operating costs in oil-producing wells. However, the accuracy of the model depends on the accuracy of the input data, and the cost of Prosper software can be high. Therefore, further research is needed to improve the accuracy of the model and evaluate the cost-effectiveness of using Prosper software in gas lift system optimization

Keywords: gas lift, prosper software, injection rate, operating costs, oil-producing wells

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Authors: Sukruthai Sapniwat, Falan Srisuriyachai

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Polymer Flooding is one of the most well-known methods in Enhanced Oil Recovery (EOR) technology which can be implemented after either primary or secondary recovery, resulting in favorable conditions for the displacement mechanism in order to lower the residual oil in the reservoir. Polymer substances can lower the mobility ratio of the whole process by increasing the viscosity of injected water. Therefore, polymer flooding can increase volumetric sweep efficiency, which leads to a better recovery factor. Moreover, polymer adsorption onto rock surface can help decrease reservoir permeability contrast with high heterogeneity. Due to the reduction of the absolute permeability, effective permeability to water, representing flow ability of the injected fluid, is also reduced. Once polymer is adsorbed onto rock surface, polymer molecule can be desorbed when different fluids are injected. This study is performed to evaluate the effects of the adsorption and desorption process of polymer solutions to yield benefits on the oil recovery mechanism. A reservoir model is constructed by reservoir simulation program called STAR® commercialized by the Computer Modeling Group (CMG). Various polymer concentrations, starting times of polymer flooding process and polymer injection rates were evaluated with selected values of polymer desorption degrees including 0, 25, 50, 75 and 100%. The higher the value, the more adsorbed polymer molecules to return back to flowing fluid. According to the results, polymer desorption lowers polymer consumption, especially at low concentrations. Furthermore, starting time of polymer flooding and injection rate affect the oil production. The results show that waterflooding followed by earlier polymer flooding can increase the oil recovery factor while the higher injection rate also enhances the recovery. Polymer concentration is related to polymer consumption due to the two main benefits of polymer flooding control described above. Therefore, polymer slug size should be optimized based on polymer concentration. Polymer desorption causes polymer re-employment that is previously adsorbed onto rock surface, resulting in an increase of sweep efficiency in the further period of polymer flooding process. Even though waterflooding supports polymer injectivity, water cut at the producer can prematurely terminate the oil production. The injection rate decreases polymer adsorption due to decreased retention time of polymer flooding process.

Keywords: enhanced oil recovery technology, polymer adsorption and desorption, polymer flooding, reservoir simulation

Procedia PDF Downloads 323

Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Nikolaos Reppas, Yilin Gui

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A dual-porosity finite element-code will be presented for the stability analysis of the wellbore during CO₂ injection. An elastoplastic damage response will be considered to the model. The Finite Element Method (FEM) will be validated using experimental results from literature or from experiments that are planned to be undertaken at Newcastle University. The main target of the research paper is to present a constitutive model that can help industries to safely store CO₂ in geological rock formations and forecast any changes on the surrounding rock of the wellbore. The fully coupled elastoplastic damage Thermo-Hydraulic-Mechanical (THM) model will determine the pressure and temperature of the injected CO₂ as well as the size of the radius of the wellbore that can make the Carbon Capture and Storage (CCS) procedure more efficient.

Keywords: carbon capture and storage, Wellbore stability, elastoplastic damage response for rock, constitutive THM model, fully coupled thermo-hydraulic-mechanical model

Procedia PDF Downloads 168

Authors: Jularat Chumnaul

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In forensic science, corpses from various homicides are different; there are both complete and incomplete, depending on causes of death or forms of homicide. For example, some corpses are cut into pieces, some are camouflaged by dumping into the river, some are buried, some are burned to destroy the evidence, and others. If the corpses are incomplete, it can lead to the difficulty of personally identifying because some tissues and bones are destroyed. To specify gender of the corpses from skeletal remains, the most precise method is DNA identification. However, this method is costly and takes longer so that other identification techniques are used instead. The first technique that is widely used is considering the features of bones. In general, an evidence from the corpses such as some pieces of bones, especially the skull and pelvis can be used to identify their gender. To use this technique, forensic scientists are required observation skills in order to classify the difference between male and female bones. Although this technique is uncomplicated, saving time and cost, and the forensic scientists can fairly accurately determine gender by using this technique (apparently an accuracy rate of 90% or more), the crucial disadvantage is there are only some positions of skeleton that can be used to specify gender such as supraorbital ridge, nuchal crest, temporal lobe, mandible, and chin. Therefore, the skeletal remains that will be used have to be complete. The other technique that is widely used for gender specifying in forensic science and archeology is skeletal measurements. The advantage of this method is it can be used in several positions in one piece of bones, and it can be used even if the bones are not complete. In this study, the classification and cluster analysis are applied to this technique, including the Kth Nearest Neighbor Classification, Classification Tree, Ward Linkage Cluster, K-mean Cluster, and Two Step Cluster. The data contains 507 particular individuals and 9 skeletal measurements (diameter measurements), and the performance of five methods are investigated by considering the apparent error rate (APER). The results from this study indicate that the Two Step Cluster and Kth Nearest Neighbor method seem to be suitable to specify gender from human skeletal remains because both yield small apparent error rate of 0.20% and 4.14%, respectively. On the other hand, the Classification Tree, Ward Linkage Cluster, and K-mean Cluster method are not appropriate since they yield large apparent error rate of 10.65%, 10.65%, and 16.37%, respectively. However, there are other ways to evaluate the performance of classification such as an estimate of the error rate using the holdout procedure or misclassification costs, and the difference methods can make the different conclusions.

Keywords: skeletal measurements, classification, cluster, apparent error rate

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: Javier Alfonso Bonilla-Chávez, Angélica Lozano

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In North America, more than 2,000 people annually die in accidents related to railroad tracks. In 2020, collisions at grade crossings were the main cause of deaths related to railway accidents in Mexico. Railway networks have constant interaction with motor transport users, cyclists, and pedestrians, mainly in grade crossings, where is the greatest vulnerability and risk of accidents. Usually, accidents at grade crossings are directly related to risky behavior and non-compliance with regulations by motorists, cyclists, and pedestrians, especially in developing countries. Around the world, countries classify these crossings in different ways. In Mexico, according to their dangerousness (high, medium, or low), types A, B and C have been established, recommending for each one different type of auditive and visual signaling and gates, as well as horizontal and vertical signaling. This classification is based in a weighting, but regrettably, it is not explained how the weight values were obtained. A review of the variables and the current approach for the grade crossing classification is required, since it is inadequate for some crossings. In contrast, North America (USA and Canada) and European countries consider a broader classification so that attention to each crossing is addressed more precisely and equipment costs are adjusted. Lack of a proper classification, could lead to cost overruns in the equipment and a deficient operation. To exemplify the lack of a good classification, six crossings are studied, three located in the rural area of Mexico and three in Mexico City. These cases show the need of: improving the current regulations, improving the existing infrastructure, and implementing technological systems, including informative signals with nomenclature of the involved crossing and direct telephone line for reporting emergencies. This implementation is unaffordable for most municipal governments. Also, an inventory of the most dangerous grade crossings in urban and rural areas must be obtained. Then, an approach for improving the classification of grade crossings is suggested. This approach must be based on criteria design, characteristics of adjacent roads or intersections which can influence traffic flow through the crossing, accidents related to motorized and non-motorized vehicles, land use and land management, type of area, and services and economic activities in the zone where the grade crossings is located. An expanded classification of grade crossing in Mexico could reduce accidents and improve the efficiency of the railroad.

Keywords: accidents, grade crossing, railroad, traffic safety

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

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Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

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Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: convolutional neural network, lithology, prediction of reservoir, seismic attributes

Procedia PDF Downloads 173

Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: José A. Menezes, Giordano Cabral, Bruno T. Gomes

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Choosing good features is an essential part of machine learning. Recent techniques aim to automate this process. For instance, feature learning intends to learn the transformation of raw data into a useful representation to machine learning tasks. In automatic audio classification tasks, this is interesting since the audio, usually complex information, needs to be transformed into a computationally convenient input to process. Another technique tries to generate features by searching a feature space. Genetic algorithms, for instance, have being used to generate audio features by combining or modifying them. We find this approach particularly interesting and, despite the undeniable advances of feature learning approaches, we wanted to take a step forward in the use of genetic algorithms to find audio features, combining them with more conventional methods, like PCA, and inserting search control mechanisms, such as constraints over a confusion matrix. This work presents the results obtained on particular audio classification problems.

Keywords: feature generation, feature learning, genetic algorithm, music information retrieval

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Authors: Omid Ghollipoor Bashiri, Farzam Hatefi

Abstract:

Dichlorvos a synthetic organophosphate poisons are used as insecticide. These toxins can be used insecticides in agriculture and medicine for destruction and/or eradication of ectoparasites of animals. Studies have shown that Dichlorvos creation seizure effects in different animals. Amlodipine, dihydropyridine calcium channel blockers, widely used for treatment of cardiovascular diseases. Studies have shown that the calcium channel blockers are anticonvulsant effects in different animal models. The aim of this study was to determine the effect of Amlodipine on Dichlorvos-induced seizures in mice. In this experiment, the animals were received different doses of Amlodipine (2.5, 5, 10, 20 and 40 mg/ kg b.wt.) intraperitoneally 30 min before intraperitoneal injection of Dichlorvos (50 mg/kg b.wt). After Dichlorvos injection, clonic and tonic seizures, and finally was the fate was investigated. Results showed that Amlodipine dose-dependently reduced the severity of Dichlorvos-induced seizures, so that Amlodipine at a dose of 5mg (The lowest, p<0.05) and 40 mg/kg b.wt. (The highest, p<0.001) which had anticonvulsant effects. The anticonvulsant activity of Amlodipine suggests that possibly due to the antagonistic effect on voltage-dependent calcium channel.

Keywords: dichlorvos, amlodipine, seizures, mice

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Authors: Pawan Kumar, Pitamber Singh Negi

Abstract:

Many of the regeneration failures in conifers are often being attributed to heavy insect attack and pathogens during the period of seed formation and under storage conditions. Conifer berries and seed insects occur throughout the known range of the hosts and also limit the production of seed for nursery stock. On occasion, even entire seed crops are lost due to insect attacks. The berry and seeds of both the species have been found to be infected with insects. Recently, heavy damage to the berry and seeds of Juniper and Chilgoza Pine was observed in the field as well as in stored conditions, leading to reduction in the viability of seeds to germinate. Both the species are under great threat and regeneration of the species is very low. Due to lack of adequate literature, the study on the damage potential of seed insects was urgently required to know the exact status of the insect-pests attacking seeds/berries of both the pine species so as to develop pest management practices against the insect pests attack. As both the species are also under threat and are fighting for survival, so the study is important to develop management practices for the insect-pests of seeds/berries of Juniper and Chilgoza pine so as to evaluate in the nursery, as these species form major vegetation of their distribution zones. A six-year study on the management of insect pests of seeds of Chilgoza revealed that seeds of this species are prone to insect pests mainly borers. During present investigations, it was recorded that cones of are heavily attacked only by Dioryctria abietella (Lepidoptera: Pyralidae) in natural conditions, but seeds which are economically important are heavily infected, (sometimes up to 100% damage was also recorded) by insect borer, Plodia interpunctella (Lepidoptera: Pyralidae) and is recorded for the first time ‘to author’s best knowledge’ infesting the stored Chilgoza seeds. Similarly, Juniper berries and seeds were heavily attacked only by a single borer, Homaloxestis cholopis (Lepidoptera: Lecithoceridae) recorded as a new report in natural habitat as well as in stored conditions. During the present investigation details of insect pest attack on Juniper and Chilgoza pine seeds and berries was observed and suitable management practices were also developed to contain the insect-pests attack.

Keywords: borer, chilgozapine, cones, conifer, Lepidoptera, juniper, management, seed

Procedia PDF Downloads 144

Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

Procedia PDF Downloads 139

Authors: Aleksandra Zysk, Pawel Badura

Abstract:

Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

Procedia PDF Downloads 299

Authors: K. Bhojnadh, M. Fiddler, D. Cheshire

Abstract:

An investigation into the Kutta effect on the trailing edge of a subsonic aerofoil was conducted which led to an analysis using Ansys Fluent to determine the effect of flow separation over a NACA 0012 aerofoil. This aerofoil was subjected to oscillations to create an unsteady flow over the aerofoil, therefore, creating turbulence, with unsteady aerodynamics playing a key role to determine the flow regimes when the aerofoil is subjected to different angles of attack along with varying Reynolds numbers. Many theories were evolved to determine the flow parameters of a 2-D aerofoil in these unsteady conditions because they behave unpredictably at the trailing edge when subjected to a different angle of attack. The shear area observed in the boundary layer at the trailing edge tends towards an unsteady turbulent flow even at small angles of attack, creating drag as the flow separates, reducing the aerodynamic performance of aerofoil. In this paper, research was conducted to determine the effect of Kutta circulation over the aerofoil and the effect of that circulation in reducing the effect of pressure and boundary layer distribution over the aerofoil. The effect of circulation is observed by using Ansys Fluent by using varying flow parameters and differential schemes to observe the flow behaviour on the aerofoil. Initially, steady flow analysis was conducted on the aerofoil to determine the effect of circulation, and it was noticed that the effect of circulation could only be properly observed when the aerofoil is subjected to oscillations. Therefore, that was modelled by using Ansys user-defined functions, which define the motion of the aerofoil by creating a dynamic mesh on the aerofoil. Initial results were observed, and further development of the dynamic mesh functions in Ansys is taking place. This research will determine the overall basic principles of unsteady flow aerodynamics applied to the investigation of Kutta related circulation, and gives an indication regarding the generation of vortices which is discussed further in this paper.

Keywords: circulation, flow seperation, turbulence modelling, vortices

Procedia PDF Downloads 201

Authors: Damola O. Lawal, David W. Gresty, Diane E. Gan, Louise Hewitt

Abstract:

This paper investigates the feasibility of using a programmable USB such as the O.MG Cable to perform a file tampering attack. Here, the O.MG Cable, an apparently harmless mobile device charger, is used in an unauthorized way to alter the content of a file (accounts record-January_Contributions.xlsx). The aim is to determine if a forensics analyst can reliably determine who has altered the target file; the O.MG Cable or the user of the machine. This work highlights some of the traces of the O.MG Cable left behind on the target computer itself, such as the Product ID (PID) and Vendor ID (ID). Also discussed is the O.MG Cable’s behavior during the experiments. We determine if a forensics analyst could identify if any evidence has been left behind by the programmable device on the target file once it has been removed from the computer to establish if the analyst would be able to link the traces left by the O.MG Cable to the file tampering. It was discovered that the forensic analyst might mistake the actions of the O.MG Cable for the computer users. Experiments carried out in this work could further the discussion as to whether an innocent user could be punished for the unauthorized changes made by a programmable device.

Keywords: O.MG cable, programmable USB, file tampering attack, digital evidence credibility, miscarriage of justice, cyber fraud

Procedia PDF Downloads 153