Search results for: deep maxout network
5869 Cellular Mobile Telecommunication GSM Radio Base Station Network Planning
Authors: Saeed Alzahrani, Yaser Miaji
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The project involves the design and simulation of a Mobile Cellular Telecommunication Network using the software tool CelPlanner. The design is mainly concerned with Global System for Mobile Communications . The design and simulation of the network is done for a small part of the area allocated for us in the terrain area of Shreveport city .The project is concerned with designing a network that is cost effective and which also efficiently meets the required Grade of Service (GOS) AND Quality of Service (QOS).The expected outcome of this project is the design of a network that gives a good coverage for the area allocated to us with minimum co-channel interference and adjacent channel interference. The Handover and Traffic Handling Capacity should also be taken into consideration and should be good for the given area . The Traffic Handling Capacity of the network in a way decides whether the designed network is good or bad . The design also takes into consideration the topographical and morphological information.Keywords: mobile communication, GSM, radio base station, network planning
Procedia PDF Downloads 4345868 Mediation Role of Teachers’ Surface Acting and Deep Acting on the Relationship between Calling Orientation and Work Engagement
Authors: Yohannes Bisa Biramo
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This study examined the meditational role of surface acting and deep acting on the relationship between calling orientation and work engagement of teachers in secondary schools of Wolaita Zone, Wolaita, Ethiopia. A predictive non-experimental correlational design was performed among 300 secondary school teachers. Stratified random sampling followed by a systematic random sampling technique was used as the basis for selecting samples from the target population. To analyze the data, Structural Equation Modeling (SEM) was used to test the association between the independent variables and the dependent variables. Furthermore, the goodness of fit of the study variables was tested using SEM to see and explain the path influence of the independent variable on the dependent variable. Confirmatory factor analysis (CFA) was conducted to test the validity of the scales in the study and to assess the measurement model fit indices. The analysis result revealed that calling was significantly and positively correlated with surface acting, deep acting and work engagement. Similarly, surface acting was significantly and positively correlated with deep acting and work engagement. And also, deep acting was significantly and positively correlated with work engagement. With respect to mediation analysis, the result revealed that surface acting mediated the relationship between calling and work engagement and also deep acting mediated the relationship between calling and work engagement. Besides, by using the model of the present study, the school leaders and practitioners can identify a core area to be considered in recruiting and letting teachers teach, in giving induction training for newly employed teachers and in performance appraisal.Keywords: calling, surface acting, deep acting, work engagement, mediation, teachers
Procedia PDF Downloads 735867 Comparison of Deep Learning and Machine Learning Algorithms to Diagnose and Predict Breast Cancer
Authors: F. Ghazalnaz Sharifonnasabi, Iman Makhdoom
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Breast cancer is a serious health concern that affects many people around the world. According to a study published in the Breast journal, the global burden of breast cancer is expected to increase significantly over the next few decades. The number of deaths from breast cancer has been increasing over the years, but the age-standardized mortality rate has decreased in some countries. It’s important to be aware of the risk factors for breast cancer and to get regular check- ups to catch it early if it does occur. Machin learning techniques have been used to aid in the early detection and diagnosis of breast cancer. These techniques, that have been shown to be effective in predicting and diagnosing the disease, have become a research hotspot. In this study, we consider two deep learning approaches including: Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). We also considered the five-machine learning algorithm titled: Decision Tree (C4.5), Naïve Bayesian (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Algorithm and XGBoost (eXtreme Gradient Boosting) on the Breast Cancer Wisconsin Diagnostic dataset. We have carried out the process of evaluating and comparing classifiers involving selecting appropriate metrics to evaluate classifier performance and selecting an appropriate tool to quantify this performance. The main purpose of the study is predicting and diagnosis breast cancer, applying the mentioned algorithms and also discovering of the most effective with respect to confusion matrix, accuracy and precision. It is realized that CNN outperformed all other classifiers and achieved the highest accuracy (0.982456). The work is implemented in the Anaconda environment based on Python programing language.Keywords: breast cancer, multi-layer perceptron, Naïve Bayesian, SVM, decision tree, convolutional neural network, XGBoost, KNN
Procedia PDF Downloads 625866 Optimization of Pressure in Deep Drawing Process
Authors: Ajay Kumar Choubey, Geeta Agnihotri, C. Sasikumar, Rashmi Dwivedi
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Deep-drawing operations are performed widely in industrial applications. It is very important for efficiency to achieve parts with no or minimum defects. Deep drawn parts are used in high performance, high strength and high reliability applications where tension, stress, load and human safety are critical considerations. Wrinkling is a kind of defect caused by stresses in the flange part of the blank during metal forming operations. To avoid wrinkling appropriate blank-holder pressure/force or drawbead can be applied. Now-a-day computer simulation plays a vital role in the field of manufacturing process. So computer simulation of manufacturing has much advantage over previous conventional process i.e. mass production, good quality of product, fast working etc. In this study, a two dimensional elasto-plastic Finite Element (F.E.) model for Mild Steel material blank has been developed to study the behavior of the flange wrinkling and deep drawing parameters under different Blank-Holder Pressure (B.H.P.). For this, commercially available Finite Element software ANSYS 14 has been used in this study. Simulation results are critically studied and salient conclusions have been drawn.Keywords: ANSYS, deep drawing, BHP, finite element simulation, wrinkling
Procedia PDF Downloads 4465865 Multi-Classification Deep Learning Model for Diagnosing Different Chest Diseases
Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury
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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification
Procedia PDF Downloads 825864 Design and Realization of Computer Network Security Perception Control System
Authors: El Miloudi Djelloul
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Based on analysis on applications by perception control technology in computer network security status and security protection measures, from the angles of network physical environment and network software system environmental security, this paper provides network security system perception control solution using Internet of Things (IOT), telecom and other perception technologies. Security Perception Control System is in the computer network environment, utilizing Radio Frequency Identification (RFID) of IOT and telecom integration technology to carry out integration design for systems. In the network physical security environment, RFID temperature, humidity, gas and perception technologies are used to do surveillance on environmental data, dynamic perception technology is used for network system security environment, user-defined security parameters, security log are used for quick data analysis, extends control on I/O interface, by development of API and AT command, Computer Network Security Perception Control based on Internet and GSM/GPRS is achieved, which enables users to carry out interactive perception and control for network security environment by WEB, E-MAIL as well as PDA, mobile phone short message and Internet. In the system testing, through middle ware server, security information data perception in real time with deviation of 3-5% was achieved; it proves the feasibility of Computer Network Security Perception Control System.Keywords: computer network, perception control system security strategy, Radio Frequency Identification (RFID)
Procedia PDF Downloads 4395863 End-to-End Spanish-English Sequence Learning Translation Model
Authors: Vidhu Mitha Goutham, Ruma Mukherjee
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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation
Procedia PDF Downloads 1705862 Virtual Routing Function Allocation Method for Minimizing Total Network Power Consumption
Authors: Kenichiro Hida, Shin-Ichi Kuribayashi
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In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions which are one of critical network functions, and presents the virtual routing function allocation algorithm that minimizes the total power consumption. In addition, this study presents the useful allocation policy of virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy takes the ratio of the power consumption of a routing function to that of a circuit and traffic distribution between areas into consideration. Furthermore, the present paper shows that there are cases where the use of NFV-based routing functions makes it possible to reduce the total power consumption dramatically, in comparison to a conventional network, in which it is not economically viable to distribute small-capacity routing functions.Keywords: NFV, resource allocation, virtual routing function, minimum power consumption
Procedia PDF Downloads 3375861 Accurate Mass Segmentation Using U-Net Deep Learning Architecture for Improved Cancer Detection
Authors: Ali Hamza
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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network
Procedia PDF Downloads 745860 The Impact of Artificial Intelligence on Agricultural Machines and Plant Nutrition
Authors: Kirolos Gerges Yakoub Gerges
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Self-sustaining agricultural machines act in stochastic surroundings and therefore, should be capable of perceive the surroundings in real time. This notion can be done using image sensors blended with superior device learning, mainly Deep mastering. Deep convolutional neural networks excel in labeling and perceiving colour pix and since the fee of RGB-cameras is low, the hardware cost of accurate notion relies upon heavily on memory and computation power. This paper investigates the opportunity of designing lightweight convolutional neural networks for semantic segmentation (pixel clever class) with reduced hardware requirements, to allow for embedded usage in self-reliant agricultural machines. The usage of compression techniques, a lightweight convolutional neural community is designed to carry out actual-time semantic segmentation on an embedded platform. The community is skilled on two big datasets, ImageNet and Pascal Context, to apprehend as much as four hundred man or woman instructions. The 400 training are remapped into agricultural superclasses (e.g. human, animal, sky, road, area, shelterbelt and impediment) and the capacity to provide correct actual-time perception of agricultural environment is studied. The network is carried out to the case of self-sufficient grass mowing the usage of the NVIDIA Tegra X1 embedded platform. Feeding case-unique pics to the community consequences in a fully segmented map of the superclasses within the picture. As the network remains being designed and optimized, handiest a qualitative analysis of the technique is entire on the abstract submission deadline. intending this cut-off date, the finalized layout is quantitatively evaluated on 20 annotated grass mowing pictures. Light-weight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show aggressive performance on the subject of accuracy and speed. It’s miles viable to offer value-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.Keywords: centrifuge pump, hydraulic energy, agricultural applications, irrigationaxial flux machines, axial flux applications, coreless machines, PM machinesautonomous agricultural machines, deep learning, safety, visual perception
Procedia PDF Downloads 125859 Assessing the Effectiveness of Machine Learning Algorithms for Cyber Threat Intelligence Discovery from the Darknet
Authors: Azene Zenebe
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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science
Procedia PDF Downloads 1455858 A Survey of Attacks and Security Requirements in Wireless Sensor Networks
Authors: Vishnu Pratap Singh Kirar
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Wireless sensor network (WSN) is a network of many interconnected networked systems, they equipped with energy resources and they are used to detect other physical characteristics. On WSN, there are many researches are performed in past decades. WSN applicable in many security systems govern by military and in many civilian related applications. Thus, the security of WSN gets attention of researchers and gives an opportunity for many future aspects. Still, there are many other issues are related to deployment and overall coverage, scalability, size, energy efficiency, quality of service (QoS), computational power and many more. In this paper we discus about various applications and security related issue and requirements of WSN.Keywords: wireless sensor network (WSN), wireless network attacks, wireless network security, security requirements
Procedia PDF Downloads 4825857 Deep Convolutional Neural Network for Detection of Microaneurysms in Retinal Fundus Images at Early Stage
Authors: Goutam Kumar Ghorai, Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, G. Sarkar, Ashis K. Dhara
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Diabetes mellitus is one of the most common chronic diseases in all countries and continues to increase in numbers significantly. Diabetic retinopathy (DR) is damage to the retina that occurs with long-term diabetes. DR is a major cause of blindness in the Indian population. Therefore, its early diagnosis is of utmost importance towards preventing progression towards imminent irreversible loss of vision, particularly in the huge population across rural India. The barriers to eye examination of all diabetic patients are socioeconomic factors, lack of referrals, poor access to the healthcare system, lack of knowledge, insufficient number of ophthalmologists, and lack of networking between physicians, diabetologists and ophthalmologists. A few diabetic patients often visit a healthcare facility for their general checkup, but their eye condition remains largely undetected until the patient is symptomatic. This work aims to focus on the design and development of a fully automated intelligent decision system for screening retinal fundus images towards detection of the pathophysiology caused by microaneurysm in the early stage of the diseases. Automated detection of microaneurysm is a challenging problem due to the variation in color and the variation introduced by the field of view, inhomogeneous illumination, and pathological abnormalities. We have developed aconvolutional neural network for efficient detection of microaneurysm. A loss function is also developed to handle severe class imbalance due to very small size of microaneurysms compared to background. The network is able to locate the salient region containing microaneurysms in case of noisy images captured by non-mydriatic cameras. The ground truth of microaneurysms is created by expert ophthalmologists for MESSIDOR database as well as private database, collected from Indian patients. The network is trained from scratch using the fundus images of MESSIDOR database. The proposed method is evaluated on DIARETDB1 and the private database. The method is successful in detection of microaneurysms for dilated and non-dilated types of fundus images acquired from different medical centres. The proposed algorithm could be used for development of AI based affordable and accessible system, to provide service at grass root-level primary healthcare units spread across the country to cater to the need of the rural people unaware of the severe impact of DR.Keywords: retinal fundus image, deep convolutional neural network, early detection of microaneurysms, screening of diabetic retinopathy
Procedia PDF Downloads 1335856 Artificial Intelligence in Bioscience: The Next Frontier
Authors: Parthiban Srinivasan
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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction
Procedia PDF Downloads 3525855 Pilot-free Image Transmission System of Joint Source Channel Based on Multi-Level Semantic Information
Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu
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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness
Procedia PDF Downloads 1115854 High-Capacity Image Steganography using Wavelet-based Fusion on Deep Convolutional Neural Networks
Authors: Amal Khalifa, Nicolas Vana Santos
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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.Keywords: deep learning, steganography, image, discrete wavelet transform, fusion
Procedia PDF Downloads 765853 Hydrogeochemical Characteristics of the Different Aquiferous Layers in Oban Basement Complex Area (SE Nigeria)
Authors: Azubuike Ekwere
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The shallow and deep aquiferous horizons of the fractured and weathered crystalline basement Oban Massif of south-eastern Nigeria were studied during the dry and wet seasons. The criteria were ascertaining hydrochemistry relative to seasonal and spatial variations across the study area. Results indicate that concentrations of major cations and anions exhibit the order of abundance; Ca>Na>Mg>K and HCO3>SO4>Cl respectively, with minor variations across sampling seasons. Major elements Ca, Mg, Na and K were higher for the shallow aquifers than the deep aquifers across seasons. The major anions Cl, SO4, HCO3, and NO3 were higher for the deep aquifers compared to the shallow ones. Two water types were identified for both aquifer types: Ca-Mg-HCO3 and Ca-Na-Cl-SO4. Most of the parameters considered were within the international limits for drinking, domestic and irrigation purposes. Assessment by use of sodium absorption ratio (SAR), percent sodium (%Na) and the wilcox diagram reveals that the waters are suitable for irrigation purposes.Keywords: shallow aquifer, deep aquifer, seasonal variation, hydrochemistry, Oban massif, Nigeria
Procedia PDF Downloads 6535852 Protection Plan of Medium Voltage Distribution Network in Tunisia
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The distribution networks are often exposed to harmful incidents which can halt the electricity supply of the customer. In this context, we studied a real case of a critical zone of the Tunisian network which is currently characterized by the dysfunction of its plan of protection. In this paper, we were interested in the harmonization of the protection plan settings in order to ensure a perfect selectivity and a better continuity of service on the whole of the network.Keywords: distribution network Gabes-Tunisia, continuity of service, protection plan settings, selectivity
Procedia PDF Downloads 5045851 Restoring Sagging Neck with Minimal Scar Face Lifting
Authors: Alessandro Marano
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The author describes the use of deep plane face lifting and platysmaplasty to treat sagging neck with minimal scars. Series of case study. The author uses a selective deep plane face lift with a minimal access scar that not extend behind the ear lobe, neck liposuction and platysmaplasty to restore the sagging neck; the scars are minimal and no require drainage post-op. The deep plane face lifting can achieve a good result restoring vertical vectors in aging and sagging face, neck district can be treated without cutting the skin behind the ear lobe combining the SMAS vertical suspension and platysmaplasty; surgery can be performed in local anesthesia with sedation in day surgery and fast recovery. Restoring neck sagging without extend scars behind ear lobe is possible in selected patients, procedure is fast, safe, no drainage required, patients are satisfied and healing time is fast and comfortable.Keywords: face lifting, aesthetic, face, neck, platysmaplasty, deep plane
Procedia PDF Downloads 945850 Morphological Processing of Punjabi Text for Sentiment Analysis of Farmer Suicides
Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney
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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis
Procedia PDF Downloads 3135849 Preparation of Papers - Developing a Leukemia Diagnostic System Based on Hybrid Deep Learning Architectures in Actual Clinical Environments
Authors: Skyler Kim
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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning
Procedia PDF Downloads 1815848 Monitoring the Effect of Deep Frying and the Type of Food on the Quality of Oil
Authors: Omar Masaud Almrhag, Frage Lhadi Abookleesh
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Different types of food like banana, potato and chicken affect the quality of oil during deep fat frying. The changes in the quality of oil were evaluated and compared. Four different types of edible oils, namely, corn oil, soybean, canola, and palm oil were used for deep fat frying at 180°C ± 5°C for 5 h/d for six consecutive days. A potato was sliced into 7-8 cm length wedges and chicken was cut into uniform pieces of 100 g each. The parameters used to assess the quality of oil were total polar compound (TPC), iodine value (IV), specific extinction E1% at 233 nm and 269 nm, fatty acid composition (FAC), free fatty acids (FFA), viscosity (cp) and changes in the thermal properties. Results showed that, TPC, IV, FAC, Viscosity (cp) and FFA composition changed significantly with time (P< 0.05) and type of food. Significant differences (P< 0.05) were noted for the used parameters during frying of the above mentioned three products.Keywords: frying potato, chicken, frying deterioration, quality of oil
Procedia PDF Downloads 4145847 Quality and Quantity in the Strategic Network of Higher Education Institutions
Authors: Juha Kettunen
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The study analyzes the quality and the size of the strategic network of higher education institutions and the concept of fitness for purpose in quality assurance. It also analyses the transaction costs of networking that have consequences on the number of members in the network. Empirical evidence is presented from the Consortium on Applied Research and Professional Education, which is a European strategic network of six higher education institutions. The results of the study support the argument that the number of members in the strategic network should be relatively small to provide high-quality results. The practical importance is that networking has been able to promote international research and development projects. The results of this study are important for those who want to design and improve international networks in higher education.Keywords: higher education, network, research and development, strategic management
Procedia PDF Downloads 3375846 Energy Efficient Firefly Algorithm in Wireless Sensor Network
Authors: Wafa’ Alsharafat, Khalid Batiha, Alaa Kassab
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Wireless sensor network (WSN) is comprised of a huge number of small and cheap devices known as sensor nodes. Usually, these sensor nodes are massively and deployed randomly as in Ad-hoc over hostile and harsh environment to sense, collect and transmit data to the needed locations (i.e., base station). One of the main advantages of WSN is that the ability to work in unattended and scattered environments regardless the presence of humans such as remote active volcanoes environments or earthquakes. In WSN expanding network, lifetime is a major concern. Clustering technique is more important to maximize network lifetime. Nature-inspired algorithms are developed and optimized to find optimized solutions for various optimization problems. We proposed Energy Efficient Firefly Algorithm to improve network lifetime as long as possible.Keywords: wireless network, SN, Firefly, energy efficiency
Procedia PDF Downloads 3845845 Employing Bayesian Artificial Neural Network for Evaluation of Cold Rolling Force
Authors: P. Kooche Baghy, S. Eskandari, E.javanmard
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Neural network has been used as a predictive means of cold rolling force in this dissertation. Thus, imposed average force on rollers as a mere input and five pertaining parameters to its as a outputs are regarded. According to our study, feed-forward multilayer perceptron network has been selected. Besides, Bayesian algorithm based on the feed-forward back propagation method has been selected due to noisy data. Further, 470 out of 585 all tests were used for network learning and others (115 tests) were considered as assessment criteria. Eventually, by 30 times running the MATLAB software, mean error was obtained 3.84 percent as a criteria of network learning. As a consequence, this the mentioned error on par with other approaches such as numerical and empirical methods is acceptable admittedly.Keywords: artificial neural network, Bayesian, cold rolling, force evaluation
Procedia PDF Downloads 4315844 How to Modernise the European Competition Network (ECN)
Authors: Dorota Galeza
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This paper argues that networks, such as the ECN and the American network, are affected by certain small events which are inherent to path dependence and preclude the full evolution towards efficiency. It is advocated that the American network is superior to the ECN in many respects due to its greater flexibility and longer history. This stems in particular from the creation of the American network, which was based on a small number of cases. Such a structure encourages further changes and modifications which are not necessarily radical. The ECN, by contrast, was established by legislative action, which explains its rigid structure and resistance to change. This paper is an attempt to transpose the superiority of the American network on to the ECN. It looks at concepts such as judicial cooperation, harmonisation of procedure, peer review and regulatory impact assessments (RIAs), and dispute resolution procedures.Keywords: antitrust, competition, networks, path dependence
Procedia PDF Downloads 3065843 A Neural Network Classifier for Identifying Duplicate Image Entries in Real-Estate Databases
Authors: Sergey Ermolin, Olga Ermolin
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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking
Procedia PDF Downloads 3305842 Enhanced Constraint-Based Optical Network (ECON) for Enhancing OSNR
Authors: G. R. Kavitha, T. S. Indumathi
Abstract:
With the constantly rising demands of the multimedia services, the requirements of long haul transport network are constantly changing in the area of optical network. Maximum data transmission using optimization of the communication channel poses the biggest challenge. Although there has been a constant focus on this area from the past decade, there was no evidence of a significant result that has been accomplished. Hence, after reviewing some potential design of optical network from literatures, it was understood that optical signal to noise ratio was one of the elementary attributes that can define the performance of the optical network. In this paper, we propose a framework termed as ECON (Enhanced Constraint-based Optical Network) that primarily optimize the optical signal to noise ratio using ROADM. The simulation is performed in Matlab and optical signal to noise ratio is extracted considering the system matrix. The outcome of the proposed study shows that optimized OSNR as compared to the existing studies.Keywords: component, optical network, reconfigurable optical add-drop multiplexer, optical signal-to-noise ratio
Procedia PDF Downloads 4825841 A Proposed Algorithm for Obtaining the Map of Subscribers’ Density Distribution for a Mobile Wireless Communication Network
Authors: C. Temaneh-Nyah, F. A. Phiri, D. Karegeya
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This paper presents an algorithm for obtaining the map of subscriber’s density distribution for a mobile wireless communication network based on the actual subscriber's traffic data obtained from the base station. This is useful in statistical characterization of the mobile wireless network.Keywords: electromagnetic compatibility, statistical analysis, simulation of communication network, subscriber density
Procedia PDF Downloads 3075840 Twitter Ego Networks and the Capital Markets: A Social Network Analysis Perspective of Market Reactions to Earnings Announcement Events
Authors: Gregory D. Saxton
Abstract:
Networks are everywhere: lunch ties among co-workers, golfing partnerships among employees, interlocking board-of-director connections, Facebook friendship ties, etc. Each network varies in terms of its structure -its size, how inter-connected network members are, and the prevalence of sub-groups and cliques. At the same time, within any given network, some network members will have a more important, more central position on account of their greater number of connections or their capacity as “bridges” connecting members of different network cliques. The logic of network structure and position is at the heart of what is known as social network analysis, and this paper applies this logic to the study of the stock market. Using an array of data analytics and machine learning tools, this study will examine 17 million Twitter messages discussing the stocks of the firms in the S&P 1,500 index in 2018. Each of these 1,500 stocks has a distinct Twitter discussion network that varies in terms of core network characteristics such as size, density, influence, norms and values, level of activity, and embedded resources. The study’s core proposition is that the ultimate effect of any market-relevant information is contingent on the characteristics of the network through which it flows. To test this proposition, this study operationalizes each of the core network characteristics and examines their influence on market reactions to 2018 quarterly earnings announcement events.Keywords: data analytics, investor-to-investor communication, social network analysis, Twitter
Procedia PDF Downloads 110