Search results for: common vector approach

Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: H. Yazdani, K. Ghorbanian

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This paper deals with modeling and simulation of the plasma actuator with OpenFOAM. Plasma actuator is one of the newest devices in flow control techniques which can delay separation by inducing external momentum to the boundary layer of the flow. The effects of the plasma actuators on the external flow are incorporated into Navier-Stokes computations as a body force vector which is obtained as a product of the net charge density and the electric field. In order to compute this body force vector, the model solves two equations: One for the electric field due to the applied AC voltage at the electrodes and the other for the charge density representing the ionized air. The simulation result is compared to the experimental and typical values which confirms the validity of the modeling.

Keywords: active flow control, flow-field, OpenFOAM, plasma actuator

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Authors: Hafsa Memona, Farkhanda Manzoor

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Cockroaches are termed as medically important pests because of their wide distribution in human habitation including houses, hospitals, food industries and kitchens. They may harbor multiple drug resistant pathogenic bacteria and protozoan parasites on their external surfaces, disseminate on human food and cause serious diseases and allergies to human. Hence, they are regarded as mechanical vector in human habitation due to their nocturnal activity and nutritional behavior. Viable eggs and dormant cysts of parasites can hitch a ride on cockroaches. Ova and cysts of parasitic organism may settle into the crevices and cracks between thorax and head. There are so many fissures and clefts and crannies on a cockroach which provide site for these organisms. This study aimed with identifying role of cockroaches in mechanically transmitting and disseminating gastrointestinal parasites in two environmental settings; hospitals and houses in urban area of Lahore. Totally, 250 adult cockroaches were collected from houses and hospitals by sticky traps and food baited traps and screened for parasitic load. All cockroaches were captured during their feeding time in natural habitat. Direct wet smear, 1% lugols iodine and modified acid-fast bacilli staining were used to identify the parasites from the body surfaces of cockroaches. Among human habitation two common species of cockroaches were collected i.e. P. americana and B. germanica. The results showed that 112 (46.8%) cockroaches harbored at least one human intestinal parasite on their body surfaces. The cockroaches from hospital environment harboured more parasites than houses. 47 (33.57%) cockroaches from houses and 65 (59.09%) from hospitals were infected with parasitic organisms. Of these, 76 (67.85%) were parasitic protozoans and 36(32.15%) were pathogenic and non-pathogenic intestinal parasites. P. americana harboured more parasites as compared to B. germanica in both environment. Most common human intestinal parasites found on cockroaches include ova of Ascaris lumbricoides (giant roundworm), Trichuris trichura (whipworm), Anchylostoma deodunalae (hookworm), Enterobius vermicularis (pinworm), Taenia spp. and Strongyloides stercoralis (threadworm). The cysts of protozoans’ parasites including Balantidium coli, Entomoeba hystolitica, C. parvum, Isospora belli, Giardia duodenalis and C. cayetenensis were isolated and identified from cockroaches. Both experimental sites were significantly different in carriage of parasitic load on cockroaches. Difference in the hygienic condition of the environments, including human excrement disposal, variable habitat interacted, indoor and outdoor species, may account for the observed variation in the parasitic carriage rate of cockroaches among different experimental site. Thus a finding of this study is that Cockroaches are uniformly distributed in human habitation and act as a mechanical vector of pathogenic parasites that cause common illness such as diarrhea and bowel disorders. This fact contributes to epidemiological chain therefore control of cockroaches will significantly lessen the prevalence of illness in human. Effective control strategies will reduce the public health burden of the gastro-intestinal parasites in the developing countries.

Keywords: cockroaches, health risks, hospitals, houses, parasites, protozoans, transmission

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Authors: Dhruba Das

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In this paper, a method has been developed to construct the membership surfaces of row and column vectors and arithmetic operations of imprecise matrix. A matrix with imprecise elements would be called an imprecise matrix. The membership surface of imprecise vector has been already shown based on Randomness-Impreciseness Consistency Principle. The Randomness- Impreciseness Consistency Principle leads to defining a normal law of impreciseness using two different laws of randomness. In this paper, the author has shown row and column membership surfaces and arithmetic operations of imprecise matrix and demonstrated with the help of numerical example.

Keywords: imprecise number, imprecise vector, membership surface, imprecise matrix

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: K. D. Candido, A. Lissounov, I. Knezevic, N. Knezevic

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Background: The most commonly performed pain procedures in the USA is Lumbar Epidural Steroid Injections (LESI). There are three main types of these procedures: transforaminal (TF), interlaminar (IL) and caudal injections. It is expected for TF injections to have better outcomes than IL injections, based on the recently published systematic review. The studies presented in that review used a midline IL approach, but those with parasagittal IL approach were not taken into consideration. Our aim is to emphasize the efficacy of the lateral parasagittal (paramedian) IL approach in this review. Methods: We included five studies in this systematic review, which compared Parasagittal-IL (PIL) with either Midline-IL (MIL) or TF LESI. Total of 296 patients who had undergone different types of LESI were observed across the five studies, and the average pain and functional improvements were calculated and compared among groups. Results: Pain and function improvements with PIL approach is superior on 12 months follow up to MIL approach (53.4% vs. 14.7%) and (55% vs. 27.7%), respectively. A 12 months follow-up results between PIL and TF shows a near equivalent effectiveness for pain (58.9% vs. 63.2%) and function improvement (47.3% vs. 48.1%). An average follow-up of 17.1 days have shown better short-term pain relief for PIL than TF approach (45.8% vs. 19.2%), respectively. Number of repeated injections is lower for PIL injections than MIL. Number of weeks between 1st and 2nd injections: PIL averaged 15.8 weeks and MIL averaged 9.7 weeks. Third LESI injection is more common in TF group (30%) than PIL group (18.8%). Conclusion: Higher complication rates are associated with TF injections for which FDA7 issued an official warning. We have recorded better outcomes in pain and function improvement of Parasagittal-IL LESI as compared to midline-IL injection, in the presented systematic review. Parasagittal and TF injections have equivalent efficacy in Pain and Function improvements thus we advocate for Parasagittal-IL approach consideration as an alternative for TF injections.

Keywords: parasagital approach, lumbar, back pain, epidural steroid injection

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Mogens Saberi

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The following article focuses on a new seismic design approach for Denmark. Denmark is located in a low seismic zone and up till now a general and very simplified approach has been used to accommodate the effect of seismic loading. The current used method is presented and it is found that the approach is on the unsafe side for many building types in Denmark. The damages during time due to earth quake is presented and a seismic map for Denmark is developed and presented. Furthermore, a new design approach is suggested and compared to the existing one. The new approach is relatively simple but captures the effect of seismic loading more realistic than the existing one. The new approach is believed to the incorporated in the Danish Deign Code for building structures.

Keywords: low seismicity, new design approach, earthquakes, Denmark

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Authors: Aman Ullah Khan, Muhammad Younus, Aqil Ijaz, Muti-Ur-Rehman Khan, Sayyed Aun Muhammad, Asif Idrees, Sanan Raza, Amar Nasir

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Dengue fever and malaria are important vector-borne diseases of public health significance affecting millions of people around the globe. Dengue fever is caused by Dengue virus while malaria is caused by plasmodium protozoan. Generally, the consequences of Malaria are less severe compared to dengue fever. This study was designed to differentiate dengue fever and malaria on the basis of clinical and laboratory findings and to compare the changes in both diseases having different causative agents transmitted by the common vector. A total of 200 patients of dengue viral infection (120 males, 80 females) were included in this prospective descriptive study. The blood samples of the individuals were first screened for malaria by blood smear examination and then the negative samples were tested by anti-dengue IgM strip. The strip positive cases were further screened by IgM capture ELISA and their complete blood count including hemoglobin estimation (Hb), total and differential leukocyte counts (TLC and DLC), erythrocyte sedimentation rate (ESR) and platelet counts were performed. On the basis of the severity of signs and symptoms, dengue virus infected patients were subdivided into dengue fever (DF) and dengue hemorrhagic fever (DHF) comprising 70 and 100 confirmed patients, respectively. On the other hand, 30 patients were found infected with Malaria while overall 120 patients showed thrombocytopenia. The patients of DHF were found to have more leucopenia, raised hemoglobin level and thrombocytopenia < 50,000/µl compared to the patients belonging to DF and malaria. On the basis of the outcomes of the study, it was concluded that patients affected by DF were at a lower risk of undergoing haematological disturbance than suffering from DHF. While, the patients infected by Malaria were found to have no significant change in their blood components.

Keywords: dengue fever, blood, serum, malaria, ELISA

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Authors: Marilyn Painagan

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This study was conducted to predict the potential geographical distribution of the banana aphid (Pentalonia negronervosa) in North Cotabato through climate envelope approach of DIVA-GIS, a software for analyzing the distribution of organisms to elucidate geographic and ecological patterns. A WorldClim database that was based on weather conditions recorded last 1950 to 2000 with a spatial resolution of approximately 1x1 km. was used in the bioclimatic modelling, this database includes temperature, precipitation, evapotranspiration and bioclimatic variables which was measured at many different locations, a bioclimatic modelling was done in the study. The study revealed that the western part of Magpet and Arakan and the municipality of Antipas are at high potential risk of occurrence of banana aphid while it is not likely to occur in the municipalities of Aleosan, Midsayap, Pikit, M’lang and Tulunan. The result of this study can help developed strategies for monitoring and managing this serious pest of banana and to prepare a mitigation measures on those areas that are potential for future infestation.

Keywords: banana aphid, bioclimatic model, bunchy top, climatic envelope approach

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Authors: Mamyrbek A. Beisenbi, Nurgul M. Kissikova, Saltanat E. Beisembina, Salamat T. Suleimenova, Samal A. Kaliyeva

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The paper proposes a method for constructing a self-organizing control system for unstable and deterministic chaotic processes in the class of catastrophe “hyperbolic umbilic” for objects with m-inputs and n-outputs. The self-organizing control system is investigated by the universal gradient-velocity method of Lyapunov vector functions. The conditions for self-organization of the control system in the class of catastrophes “hyperbolic umbilic” are shown in the form of a system of algebraic inequalities that characterize the aperiodic robust stability in the stationary states of the system.

Keywords: gradient-velocity method of Lyapunov vector-functions, hyperbolic umbilic, self-organizing control system, stability

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Authors: Ghada Badr, Manar Hosny, Nuha Bintayyash, Eman Albilali, Souad Larabi Marie-Sainte

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The median problem is significantly applied to derive the most reasonable rearrangement phylogenetic tree for many species. More specifically, the problem is concerned with finding a permutation that minimizes the sum of distances between itself and a set of three signed permutations. Genomes with equal number of genes but different order can be represented as permutations. In this paper, an algorithm, namely BeamGA median, is proposed that combines a heuristic search approach (local beam) as an initialization step to generate a number of solutions, and then a Genetic Algorithm (GA) is applied in order to refine the solutions, aiming to achieve a better median with the smallest possible reversal distance from the three original permutations. In this approach, any genome rearrangement distance can be applied. In this paper, we use the reversal distance. To the best of our knowledge, the proposed approach was not applied before for solving the median problem. Our approach considers true biological evolution scenario by applying the concept of common intervals during the GA optimization process. This allows us to imitate a true biological behavior and enhance genetic approach time convergence. We were able to handle permutations with a large number of genes, within an acceptable time performance and with same or better accuracy as compared to existing algorithms.

Keywords: median problem, phylogenetic tree, permutation, genetic algorithm, beam search, genome rearrangement distance

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Authors: John Yahng, Riteesh Bookun

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Popliteal artery aneurysms (PAAs) are the most common arterial aneurysm of the periphery. It is defined as focal dilation of the artery more than 50% of the normal vessel diameter which usually varies between 7 mm to 11 mm. The most common presentation for PAAs is claudication due to luminal stenmosis secondary to mural thrombus or acute limb ischaemia due to occlusive thrombosis or distal thromboembolism. It is less common for patients to present with non-ischaemic symptoms secondary to mass effect and compression of adjacent structures, and of these, presentation with common peroneal nerve compression is particularly uncommon. We present a rare case of a 92-year-old female patient presenting with 4-month history of left foot drop with radiological evidence of common peroneal nerve compression secondary to PAA of 22 mm by21mm in size. To the best of our knowledge, this is the smallest reported popliteal aneurysm presenting with foot drop. We also present the endovascular treatment option taken in our case.

Keywords: aneurysm, foot drop, peroneal nerve, popliteal

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Mohammed Arif, Abdessalam Kifouche

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Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.

Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: Wei Zhang, Yan He, Yan Wang, Yufeng Li, Chuanpeng Hao

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Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness.

Keywords: feature analysis, machine vision, PCA, surface roughness, SVM

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Authors: Barakat Adebisi Raji

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In the workplace, Artificial Intelligence has, in recent years, emerged as a transformative technology that revolutionizes how organizations operate and perform tasks. It is a technology that has a significant impact on transportation, manufacturing, education, cyber security, robotics, agriculture, healthcare, and so many other organizations. By harnessing AI technology, workplaces can enhance productivity, streamline processes, and make more informed decisions. Given the potential of AI to change the way we work and its impact on the labor market in years to come, employers understand that it entails legal challenges and risks despite the advantages inherent in it. Therefore, as AI continues to integrate into various aspects of the workplace, understanding the legal and ethical implications becomes paramount. Also central to this study is the question of who is held liable where AI makes any defaults; the person (company) who created the AI, the person who programmed the AI algorithm or the person who uses the AI? Thus, the aim of this paper is to provide a detailed overview of how AI-related liabilities are addressed under each legal tradition and shed light on potential areas of accord and divergence between the two legal cultures. The objectives of this paper are to (i) examine the ability of Common law and Islamic law to accommodate the issues and damage caused by AI in the workplace and the legality of compensation for such injury sustained; (ii) to discuss the extent to which AI can be described as a legal personality to bear responsibility: (iii) examine the similarities and disparities between Common Law and Islamic Jurisprudence on the liability of AI in the workplace. The methodology adopted in this work was qualitative, and the method was purely a doctrinal research method where information is gathered from the primary and secondary sources of law, such as comprehensive materials found in journal articles, expert-authored books and online news sources. Comparative legal method was also used to juxtapose the approach of Islam and Common Law. The paper concludes that since AI, in its current legal state, is not recognized as a legal entity, operators or manufacturers of AI should be held liable for any damage that arises, and the determination of who bears the responsibility should be dependent on the circumstances surrounding each scenario. The study recommends the granting of legal personality to AI systems, the establishment of legal rights and liabilities for AI, the establishment of a holistic Islamic virtue-based AI ethics framework, and the consideration of Islamic ethics.

Keywords: AI, health- care, agriculture, cyber security, common law, Shari'ah

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Authors: Arun Goel

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The present paper attempts to investigate the prediction of air entrainment rate and aeration efficiency of a free over-fall jets issuing from a triangular sharp crested weir by using regression based modelling. The empirical equations, support vector machine (polynomial and radial basis function) models and the linear regression techniques were applied on the triangular sharp crested weirs relating the air entrainment rate and the aeration efficiency to the input parameters namely drop height, discharge, and vertex angle. It was observed that there exists a good agreement between the measured values and the values obtained using empirical equations, support vector machine (Polynomial and rbf) models, and the linear regression techniques. The test results demonstrated that the SVM based (Poly & rbf) model also provided acceptable prediction of the measured values with reasonable accuracy along with empirical equations and linear regression techniques in modelling the air entrainment rate and the aeration efficiency of a free over-fall jets issuing from triangular sharp crested weir. Further sensitivity analysis has also been performed to study the impact of input parameter on the output in terms of air entrainment rate and aeration efficiency.

Keywords: air entrainment rate, dissolved oxygen, weir, SVM, regression

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Emma Serwaa Obobisaa, Haibo Chen

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International trade and outward foreign direct investment are important factors which are generally recognized in the economic growth and development. Though several scholars have struggled to reveal the influence of trade and outward foreign direct investment (FDI) on economic growth, most studies utilized common econometric models such as vector autoregression and aggregated the variables, which for the most part prompts, however, contradictory and mixed results. Thus, there is an exigent need for the precise study of the trade and FDI effect of economic growth while applying strong econometric models and disaggregating the variables into its separate individual variables to explicate their respective effects on economic growth. This will guarantee the provision of policies and strategies that are geared towards individual variables to ensure sustainable development and growth. This study, therefore, seeks to examine the causal effect of China-Africa trade and Outward Foreign Direct Investment on the economic growth of Africa using a robust and recent econometric approach such as system dynamics model. Our study impanels and tests an ensemble of a group of vital variables predominant in recent studies on trade-FDI-economic growth causality: Foreign direct ınvestment, international trade and economic growth. Our results showed that the system dynamics method provides accurate statistical inference regarding the direction of the causality among the variables than the conventional method such as OLS and Granger Causality predominantly used in the literature as it is more robust and provides accurate, critical values.

Keywords: economic growth, outward foreign direct investment, system dynamics model, international trade

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Authors: Pruthvi Nath Palleti, Sarmada Madhulika Kone

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Communities are formed based on the commonalities that exist in a set of individuals and when the group comes together on identifying those commonalities and to achieve their common goals. Thus, community planning with its vision to strengthen the community mostly involves with making or remaking of commons, which results in making or remaking of communities. This paper looks into different practices of planning around the world and tried to establish a link between commoning (the act of exercising the rights over commons by commoners) and participatory approach to community planning.

Keywords: commoners, commoning, community, participatory planning, urban local bodies

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Authors: Ming Luo

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In this real world, a manufacturer may produce more than one product. The products produced by the same manufacturer may share the same type of parts, similar design, and be produced in the same factory, i.e. some common causes. From the perspective of warranty management, the frequencies of those products’ warranty claims may have statistical dependence caused by the common causes. Warranty policy optimisation in the existing research, majorly, has not considered such dependence, which may increase bias in decision making. In the market, renewing warranty policies are provided to some unrepairable products and consumer electronic products. This paper optimises the renewing warranty policy collectively in a multi-product scenario with a consideration of the dependence among the warranty claims of the products produced by the same manufacturer. The existence of the optimal solution is proved. Numerical examples are used to validate the applicability of the proposed methods.

Keywords: mean-risk framework, modern portfolio theory, renewing warranty policy, warranty policy optimisation

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Authors: John Yahng, Hansraj Riteesh Bookun

Abstract:

Popliteal artery aneurysms (PAAs) are the most common arterial aneurysm of the periphery. It is defined as focal dilation of the artery more than 50% of the normal vessel diameter which usually varies between 7 mm to 11 mm. The most common presentation for PAAs is claudication due to luminal stenosis secondary to mural thrombus or acute limb ischaemia due to occlusive thrombosis or distal thromboembolism. It is less common for patients to present with non-ischaemic symptoms secondary to mass effect and compression of adjacent structures, and of these, presentation with common peroneal nerve compression is particularly uncommon. We present a rare case of a 92-year-old female patient presenting with 4-month history of left foot drop with radiological evidence of common peroneal nerve compression secondary to PAA of 22 mm by 21mm in size. To the best of our knowledge, this is the smallest reported popliteal aneurysm presenting with foot drop. We also present the endovascular treatment option taken in our case.

Keywords: aneurysm, foot drop, peroneal nerve, popliteal

Procedia PDF Downloads 375

Authors: Kuladeep Kumar Sadevi

Abstract:

The shift towards raising global urban population has intense implications on the sustainability of the urban livelihoods. Rapid urbanization has made governments, companies and civil societies recognize that they are barely equipped to deal with growing urban demands, especially water, waste and energy management. Effective management of land, water, energy and waste at a community level should be addressed well to attain greener cities. In pursuit of Green livelihoods; various norms, codes, and green rating programmes have been followed by stakeholders at various levels. While the sustainability is being adapted at smaller scale developments, greening the urban environment at community/city level is still finding its path to reality. This is due to lack of the sense of ownership in the citizens for their immediate neighborhoods and city as a whole. This phenomenon can be well connected to the theory of 'tragedy of commons' with respect to the community engagement to manage the common pool resources. The common pool resource management has been well addressed by Elinor Ostrom, who shared the Nobel Prize in Economics in 2009 for her lifetime of scholarly work investigating how communities succeed or fail at managing common pool (finite) resources. This paper examines the applicability of Elinor Ostrom's 8 Principles for Managing a Commons, to meet urban sustainability. The key objective of this paper is to come up with a model for effective urban common pool resource management, which ultimately leads to sustainability as a whole. The paper brings out a methodology to understand various parameters involved in urban sustainability, examine the synergies of all such parameters, and application of Ostrom’s principles to correlate these parameters in order to attain effective urban resource management.

Keywords: common pool resources, green cities, green communities, participatory management, sustainable development, urban resource management, urban sustainability

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Authors: Imran Hashmi, Sipkaduwa Arachchige Sashika Sureni Wickramasooriya

Abstract:

Gene-drive technology has emerged as a promising tool for disease control by influencing the population dynamics of disease-carrying organisms. Various gene drive mechanisms, derived from global laboratory experiments, aim to strategically manage and prevent the spread of targeted diseases. One prominent strategy involves population replacement, wherein genetically modified mosquitoes are introduced to replace the existing local wild population. To enhance our understanding and aid in the design of effective release strategies, we employ a comprehensive mathematical model. The utilized approach employs agent-based modeling, enabling the consideration of individual mosquito attributes and flexibility in parameter manipulation. Through the integration of an agent-based model and a meta-population spatial approach, the dynamics of gene drive mosquito spreading in a released site are simulated. The model's outcomes offer valuable insights into future population dynamics, providing guidance for the development of informed release strategies. This research significantly contributes to the ongoing discourse on the responsible and effective implementation of gene drive technology for disease vector control.

Keywords: gene drive, agent-based modeling, disease-carrying organisms, malaria

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Authors: Habib Gorine, Rabia Saleh

Abstract:

Mobile Ad-Hoc Networks are the special type of wireless networks which share common security requirements with other networks such as confidentiality, integrity, authentication, and availability, which need to be addressed in order to secure data transfer through the network. Their routing protocols are vulnerable to various malicious attacks which could have a devastating consequence on data security. In this paper, three types of attacks such as selfish, gray hole, and black hole attacks have been applied to the two most important routing protocols in MANET named dynamic source routing and ad-hoc on demand distance vector in order to analyse and compare the impact of these attacks on the Network performance in terms of throughput, average delay, packet loss, and consumption of energy using NS2 simulator.

Keywords: MANET, wireless networks, routing protocols, malicious attacks, wireless networks simulation

Procedia PDF Downloads 290

Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

Abstract:

Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Mohammed Arif, Abdessalam Kifouche

Abstract:

Optical character recognition (OCR) has a main role in the present time. It's capable to solve many serious problems and simplify human activities. The OCR yields to 70's, since many solutions has been proposed, but unfortunately, it was supportive to nothing but Latin languages. This work proposes a system of recognition of an off-line Arabic handwriting. This system is based on a structural segmentation method and uses support vector machines (SVM) in the classification phase. We have presented a state of art of the characters segmentation methods, after that a view of the OCR area, also we will address the normalization problems we went through. After a comparison between the Arabic handwritten characters & the segmentation methods, we had introduced a contribution through a segmentation algorithm.

Keywords: OCR, segmentation, Arabic characters, PAW, post-processing, SVM

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Authors: Rafik Djemili, Hocine Bourouba, M. C. Amara Korba

Abstract:

In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods.

Keywords: brain-computer interface, motor imagery, electroencephalogram, linear discriminant analysis, support vector machine

Procedia PDF Downloads 477