Search results for: common vector approach

Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: Sarita Agarwal, Deepika Delsa Dean

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Like the task of transferring photo images to artistic images, image-to-image translation aims to translate the data to the imitated data which belongs to the target domain. Neural Style Transfer and CycleGAN are two well-known deep learning architectures used for photo image-to-art image transfer. However, studies involving these two models concentrate on one-to-one domain translation, not one-to-multi domains translation. Our study tries to investigate deep learning architectures, which can be controlled to yield multiple artistic style translation only by adding a conditional vector. We have expanded CycleGAN and constructed Conditional CycleGAN for 5 kinds of categories translation. Our study found that the architecture inserting conditional vector into the middle layer of the Generator could output multiple artistic images.

Keywords: genetic counseling, FMR1 gene, fragile x-associated primary ovarian insufficiency, premutation

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: O. Rachinel, C. Recchia, M. Bourel, B. Recchia

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Dry microfine steam (DMS) technology, developed by Laurastar, was shown to effectively eliminate a range of pathogens such as Sars-CoV-2, E. coli, S. aureus and C. Albicans. The aim of this study was to investigate the effect of DMS technology on allergens. Therefore, the application of the DMS technology was tested on two common allergens (Dermatophagoides pteronyssinus and cat allergen Fel d 1), on different inert surfaces (e.g., cotton), during 2 to 3 seconds. Quantification of the remaining allergens was performed and the reduction rates reached 100% in 3 seconds for D. pteronyssinus and 97,74% in 2 seconds for cat allergens. In conclusion, DMS showed high efficacy in the elimination of common allergens and could be seen as a natural solution to improve domestic hygiene and reduce allergies.

Keywords: steam, allergens, dust mites, pollens

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Authors: Jun Liu

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This paper introduces a new bipartite key agreement (2PKA) protocol which provides unconditionally security and lightweight. The unconditional security is stemmed from the known impossibility of distinguishing a particular solution from all possible solutions of an underdetermined system of equations. The indistinguishability prevents an adversary from inferring to the common secret-key even with the access to an unlimited amount of computing capability. This new 2PKA protocol is also lightweight because that the calculation of a common secret-key only makes use of simple modular arithmetic. This information-theoretic 2PKA scheme provides the desired features of Key Confirmation (KC), Session Key (SK) security, Know-Key (KK) security, protection of individual privacy, and uniformly distributed value of a common key under prime modulus.

Keywords: bipartite key agreement, information-theoretic cryptography, perfect security, lightweight

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

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Authors: Eunice Chiamaka Allen-Ngbale

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Prior to bill of lading (BOL), merchants travelled along with their goods; then recorded the goods in the ship’s mates’ register; and finally started selling the goods while in transit by way of BOL, indicative that BOL is negotiable. Common law doctrine of privity of contract did not allow the transfer of right to sue to a non-party to the contract. This created hardship to cargo owners, which made many jurisdictions enact laws in this regard. Bill of Lading Act 1855 (BLA) was enacted in the United Kingdom, which applied as statute of general application under section 375 Merchant Shipping Act 1990 (MSA) in Nigeria; and conferred contractual rights of the suit on consignees and endorsees, but on the passing of ownership upon or by reason of such consignment or endorsement on the shipment of the goods simultaneously. The repeal of section 375 MSA by section 439 MSA 2007 created a lacuna, and the doctrine of privity of contract is the extant law in Nigeria. The aim of this study is to evaluate laws governing the transfer of the contractual right of suit to a third party under the bill of lading in Nigeria. The specific objectives of this study are to ascertain: (i) whether the extant law of common law doctrine of privity of the contract covers the transfer of the right of suit to the third party under the bill of lading in Nigeria; (ii) impediment(s) of the common law to transfer such right in Nigeria in the absence of any legislation; (iii) the level of applicability of the doctrine of privity of contract as it relates to transfer of the contractual right of suit to third party under the bill of lading in Nigeria; and (iv) whether to proffer possible suggestion on how to fill the lacuna left by the repeal of Merchant Shipping Act 1990. This work adopted a doctrinal approach with reliance on primary and secondary source materials. It finds that the common law doctrine of privity of contract in Nigeria is retrogressive. This work recommends for amendment of the relevant statute to cure this defect/lacuna like other commonwealth nations for best international practices.

Keywords: contract of carriage by sea, doctrine of privity of contract, lawful holder of bill of lading, third party right of suit

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Authors: Fikadu Kifle Hailegeorgis

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Common bean is one of the most important sources of protein in Ethiopia and other developing countries. However, the Mexican bean weevil, Zabrotes subfasciatus (Boheman), is a major factor in the storage of common beans that causes losses. Studies were conducted to evaluate the efficacy of botanical powders of Jatropha curcas (L.), Neem/Azadrachta indica, and Parthenium hysterophorus (L) on local common bean varieties against Z subfasciatus at Melkassa Agriculture Research Center. Twenty local common bean varieties were evaluated twice against Z. Subfasciatus in a completely randomized design in three replications at the rate of 0.2g/250g of seed for each experiment. Malathion and untreated were used as standard checks. The result indicated that RAZ White and Round Yellow showed high resistance variety in experiments while Batu and Black showed high susceptible variety in experiments. Jatropha seed powder was the most effective against Z. subfasciatus. Parthenium seed powders and neem leaf powders also indicate promising results. Common beans treated with botanicals significantly (p<0.05) had a higher germination percentage than that of the untreated seed. In general, the results obtained indicated that using bean varieties (RAZ white and Round yellow) and botanicals (Jatropha) seed powder gave the best control of Z. subfasciatus.

Keywords: botanicals, malathion, resistant varieties, Z. subfasciatus

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Authors: Anita Setianingrum, Oki S. Jaya, Zuherman Rustam

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Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy.

Keywords: ANFIS, fuzzy time series, stock forecasting, SVR

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

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Authors: Yahya Jahangiri, Ali Almasi

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Currently capitalism is one of the most important economic issues discussed by great scholars around the world. But Islamic approach, regarding this issue may differ both western and eastern views. A greatest scholar in Islamic economy ‘especially in Shia’ is Martyr Muhammad Baqir Al-Sadr. He wrote “Our economy” (Iqtisaduna) to present an economic point of view according to the Islamic teachings. In this regard firstly we will mention three approaches which are common in Muslim scullers about the economic science and then the main approach which is Sadr's view is described here. His claim explains that Islam and capitalism are in conflict with each other. And finally he explains the relationship between Islam and economy and he suggests the Islamic point of view in economy and its foundations as a solution for economic problems which we face today.

Keywords: Islam, economic science, capitalism, Martyr Sadr

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Thi Thi Zin, Pyke Tin, Ikuo Kobayashi, Yoichiro Horii

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Today dairy farm experts and farmers have well recognized the importance of dairy cow Body Condition Score (BCS) since these scores can be used to optimize milk production, managing feeding system and as an indicator for abnormality in health even can be utilized to manage for having healthy calving times and process. In tradition, BCS measures are done by animal experts or trained technicians based on visual observations focusing on pin bones, pin, thurl and hook area, tail heads shapes, hook angles and short and long ribs. Since the traditional technique is very manual and subjective, the results can lead to different scores as well as not cost effective. Thus this paper proposes an algebraic geometric imaging approach for an automatic dairy cow BCS system. The proposed system consists of three functional modules. In the first module, significant landmarks or anatomical points from the cow image region are automatically extracted by using image processing techniques. To be specific, there are 23 anatomical points in the regions of ribs, hook bones, pin bone, thurl and tail head. These points are extracted by using block region based vertical and horizontal histogram methods. According to animal experts, the body condition scores depend mainly on the shape structure these regions. Therefore the second module will investigate some algebraic and geometric properties of the extracted anatomical points. Specifically, the second order polynomial regression is employed to a subset of anatomical points to produce the regression coefficients which are to be utilized as a part of feature vector in scoring process. In addition, the angles at thurl, pin, tail head and hook bone area are computed to extend the feature vector. Finally, in the third module, the extracted feature vectors are trained by using Markov Classification process to assign BCS for individual cows. Then the assigned BCS are revised by using multiple regression method to produce the final BCS score for dairy cows. In order to confirm the validity of proposed method, a monitoring video camera is set up at the milk rotary parlor to take top view images of cows. The proposed method extracts the key anatomical points and the corresponding feature vectors for each individual cows. Then the multiple regression calculator and Markov Chain Classification process are utilized to produce the estimated body condition score for each cow. The experimental results tested on 100 dairy cows from self-collected dataset and public bench mark dataset show very promising with accuracy of 98%.

Keywords: algebraic geometric imaging approach, body condition score, Markov classification, polynomial regression

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Authors: Alina Murtishcheva

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The tendency of globalization, challenges to democracy and peace caused by the Russian invasion of Ukraine, and other global conflicts require searching general orientations of governmental development, including local government. The formation of a common theoretical framework for local government guarantees not only of harmonisation of European legislation but also creates prerequisites for the integration of new members into the European Union. One of the most important milestones of such a theoretical framework is the concept of decentralization. Decentralization as a phenomenon is characteristic of most European Union countries at different historical stages. For Ukraine, as a country that has clearly defined a European integration vector of development, understanding not only the legal but also the theoretical basis of decentralisation processes in European countries is an important prerequisite for further reforms. Decentralisation takes different forms, which leads to a variety of understandings in doctrine and, consequently, different interpretations in national legislation. Despite of this, decentralisation is based on common ideas and values such as democracy, participation, the rule of law, and proximity government that are shared by all EU member states. Nevertheless, not all EU countries are currently implementing broad decentralization in their political and legal practices. Some countries are gradually moving in this direction, while others remain quite centralised. There is also a new, insufficiently studied trend today – recentralisation, which can be broadly defined as the strengthening of centralization tendencies in countries that were considered to be decentralized. Consequently, an exploratory theoretical study is needed to identify how the concept of decentralization is combined with the recentralization tendency in EU member states. The purpose of this study is to empirically analyse scientific approaches to the concept of “decentralisation”, to highlight the tendency of recentralisation and its consequences, to analyse Ukraine's experience in the field of decentralisation of public power, and to outline the prospects for further development of Ukrainian legislation in this area.

Keywords: centralization, decentralization, local government, recentralization, reforms

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Authors: Subagyo Yotopranoto, Sri Wijayanti Sulistyawati, Sukmawati Basuki, Budi Armika, Yoes Prijatna Dachlan

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Malaria is a parasitic infectious disease that still remains a health problem in the world, including Indonesia. There is an outbreak happen at West Nusa Tenggara in 2007. A tourist spot in West Nusa Tenggara called West Lombok is mesoendemic area for malaria. Tanjung is the highest malaria morbidity subdistrict in West Lombok. Thus, the research conducted for the presence of a new species of malaria vectors, that are suspected of one factors which caused high morbidity of malaria in this region. The study was conducted in coastal and highland areas. We collected and identified Anopheles larvae from their breeding places. We also collected and identified Anopheles adult mosquitoes with outdoor cow net, indoor and outdoor human bait. In coastal area (Tembobor village), we found Anopheles vagus larvae from rivers as its breeding places. In highland area (Dasan Tengah village), we found An. subpictus from pool, lagoon, and river as its breeding places. In coastal area, with outdoor human bait, we collected An. vagus and An. subpictus adult mosquitoes. With indoor human bait, we collected An. subpictus adult mosquitoes. Whereas with outdoor cow net, we collected An. subpictus and An. maculatus, the first was more dominant. Furthermore, An subpictus strong suspected as malaria vector in coastal area. Anopheles subpictus was an anthropozoophylic mosquitoes, because it was found at indoor and outdoor places.

Keywords: malaria, vector, Tanjung, West Nusa Tenggara

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Authors: Ebrahim Asadi-Gangraj

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Hybrid flow shop scheduling problem (HFS) contains sequencing in a flow shop where, at any stage, there exist one or more related or unrelated parallel machines. This production system is a common manufacturing environment in many real industries, such as the steel manufacturing, ceramic tile manufacturing, and car assembly industries. In this research, a mixed integer linear programming (MILP) model is presented for the hybrid flow shop scheduling problem, in which, the objective consists of minimizing the maximum completion time (makespan). For this purpose, a Benders Decomposition (BD) method is developed to solve the research problem. The proposed approach is tested on some test problems, small to moderate scale. The experimental results show that the Benders decomposition approach can solve the hybrid flow shop scheduling problem in a reasonable time, especially for small and moderate-size test problems.

Keywords: hybrid flow shop, mixed integer linear programming, Benders decomposition, makespan

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Sudhir Kumar Tewatia, Kanishck Tewatia, Anttriksh Tewatia

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Advancements in soil consolidation are discussed, and further improvements are proposed with particular reference to Tewatia’s Y-Y’ Approach, which is called the Settlement versus Rate of Settlement Approach in consolidation. A branch of calculus named Y-Y' (or y versus dy/dx) is suggested (as compared to the common X-Y', x versus dy/dx, dy/dx versus x or Newton-Leibniz branch) that solves some complicated/unsolved theoretical and practical problems in physical sciences (Physics, Chemistry, Mathematics, Biology, and allied sciences) and engineering in an amazingly simple and short manner, particularly when independent variable X is unknown and X-Y' Approach can’t be used. Complicated theoretical and practical problems in 1D, 2D, 3D Primary and Secondary consolidations with non-uniform gradual loading and irregularly shaped clays are solved with elementary school level Y-Y' Approach, and it is interesting to note that in X-Y' Approach, equations become more difficult while we move from one to three dimensions, but in Y-Y' Approach even 2D/3D equations are very simple to derive, solve, and use; rather easier sometimes. This branch of calculus will have a far-reaching impact on understanding and solving the problems in different fields of physical sciences and engineering that were hitherto unsolved or difficult to be solved by normal calculus/numerical/computer methods. Some particular cases from soil consolidation that basically creeps and diffusion equations in isolation and in combination with each other are taken for comparison with heat transfer. The Y-Y’ Approach can similarly be applied in wave equations and other fields wherever normal calculus works or fails. Soil mechanics uses mathematical analogies from other fields of physical sciences and engineering to solve theoretical and practical problems; for example, consolidation theory is a replica of the heat equation from thermodynamics with the addition of the effective stress principle. An attempt is made to give them mathematical analogies.

Keywords: calculus, clay, consolidation, creep, diffusion, heat, settlement

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: V. Rashtchi, H. Bizhani, F. R. Tatari

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This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Juliani Rianto, Emma Lumby, Tracey Smith

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Cancer patients’ survival are growing with the new approach and therapy in oncology medicine. Cancer is now a new chronic disease, and rehabilitation program has become an ongoing program as part of cancer care. The focus of Cancer rehabilitation is maximising person’s physical and emotional function, stabilising general health and reducing unnecessary hospital admission. In Australia there are 150000 newly diagnosed cancer every year, and the most common Cancer are prostate, Breast, Colorectal, Melanoma and Lung Cancer. Through referral from the oncology team, we recruited cancer patient into our cancer rehabilitation program. Patients are assessed by our multi-disciplinary team including rehabilitation specialist, physiotherapist, occupational therapist, dietician, exercise physiologist, and psychologist. Specific issues are identified, including pain, side effect of chemo and radiation therapy and mental well-being. The goals were identified and reassessed every fortnight. Common goals including nutritional status, improve endurance and exercise performance, working on balance and mobility, improving emotional and vocational state, educational program for insomnia and tiredness, and reducing hospitalisation are identified and assessed. Patients are given 2 hours exercise program twice a week for 6 weeks with focus on aerobic and weight exercises and education sessions. Patients are generally benefited from the program. The quality of life is improved, support and interaction from the therapist has played an important factor in directing patient for their goals.

Keywords: cancer, exercises, benefit, mental health

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Sukryool Kang, James McConnaughey, Robin Doroshow, Raj Shekhar

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Still’s murmur, a vibratory heart murmur, is the most common normal innocent murmur of childhood. Many children with this murmur are unnecessarily referred for cardiology consultation and testing, which exacts a high cost financially and emotionally on the patients and their parents. Pediatricians to date are not successful at distinguishing Still’s murmur from murmurs of true heart disease. In this paper, we present a new algorithmic approach to distinguish Still’s murmur from pathological murmurs in children. We propose two distinct features, spectral width and signal power, which describe the sharpness of the spectrum and the signal intensity of the murmur, respectively. Seventy pediatric heart sound recordings of 41 Still’s and 29 pathological murmurs were used to develop and evaluate our algorithm that achieved a true positive rate of 97% and false positive rate of 0%. This approach would meet clinical standards in recognizing Still’s murmur.

Keywords: AR modeling, auscultation, heart murmurs, Still's murmur

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Authors: L. S. Chathurika

Abstract:

Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.

Keywords: algorithm, classification, evaluation, features, testing, training

Procedia PDF Downloads 110

Authors: Onur Yaşar, Okan Bilge, Ortaç Onmuş

Abstract:

The aim of this study is to investigate and compare the locomotion structures, especially the bone structures, of two different dolphin species, the Common bottlenose dolphin Tursiops truncatus and the Harbor porpoise Phocoena phocoena, and to provide a more detailed and descriptive comparison. To compare the structures of bones of two study species; first, the Spinous Process (SP), Inferior Articular Process (IAP), Laminae Vertebrae (LA), Foramen Vertebrae (FV), Corpus Vertebrae (CV), Transverse Process (TP) were determined and then the length of the Spinous Process (LSP), length of the Foramen Vertebrae (LFV), area of the Corpus Vertebrae (ACV), and length of the Transverse Process (LTP) were measured from the caudal view. The spine consists of a total of 61 vertebrae (7 cervical, 13 thoracic, 14 lumbar, and 27 caudal vertebrae) in the Common bottlenose dolphin, while the Harbor Porpoise has 63 vertebrae (7 cervical, 12 thoracic, 14 lumbar, 30 caudal. In the Common bottlenose dolphin, epiphyseal ossification was between the 21st caudal vertebra and the 27th caudal vertebra, while in the Harbor porpoise, it was observed in all vertebrae. Ankylosing spondylitis was observed in the C1 and C2 vertebrae in the Common bottlenose dolphin and in all cervical vertebrae between C1 and C6 in the Harbor porpoise. We argue that this difference in fused cervical vertebrae between the two species may be due to the fact that the neck movements of the Harbor porpoise in the vertical and horizontal axes are more limited than those of the Common bottlenose dolphin. We also think that as the number of fused cervical vertebrae increases, underwater maneuvers are performed at a wider angle, but to test this idea, we think that different species of dolphins should be compared and the different age groups should be investigated.

Keywords: anatomy, morphometry, vertebrae, common bottlenose dolphin, Tursiops truncatus, harbour porpoise, Phocoena phocoena

Procedia PDF Downloads 27

Authors: Hudson Akewe

Abstract:

In this study, we introduce a modified Jungck (Dual Jungck) iterative scheme and use the scheme to approximate the unique common fixed point of a pair of generalized contractive-like operators in a Banach space. The iterative scheme is also shown to be stable with respect to the maps (S,T). An example is taken to justify the convergence of the scheme. Our result is a generalization and improvement of several results in the literature on single map T.

Keywords: generalized contractive-like operators, modified Jungck iterative scheme, stability results, weakly compatible maps, unique common fixed point

Procedia PDF Downloads 335

Authors: Chelali Mohammed

Abstract:

In the context of the application of the competency-based approach in the system educational Algeria, the price of physical education and sport must privilege the acquisition of learning approaches and especially the approach science, which from problem situations, research and develops him information processing and application of knowledge and know-how in new situations in the words of ‘JOHN DEWEY’ ‘learning by practice’. And to achieve these goals and make teaching more EPS motivating, consistent and concrete, it is appropriate to perform a pedagogical approach freed from the constraints and open to creativity and student-centered in the light of the competency approach adopted in the formal curriculum. This approach is not unusual, but we think it is a highly professional nature requires the competence of the teacher.

Keywords: approach competencies, physical, education, teachers

Procedia PDF Downloads 593