Search results for: neighbor effect

Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: Alexander David Natanson

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This paper focuses on the effect of Sanctuary and Immigration and Customs Enforcement (ICE) policies on local economies. "Sanctuary cities" refers to municipal jurisdictions that limit their cooperation with the federal government's efforts to enforce immigration. Using county-level data from the American Community Survey and ICE data on economic indicators from 2006 to 2018, this study isolates the effects of local immigration policies on U.S. counties. The investigation is accomplished by simultaneously studying the policies' effects in counties where immigrants' families are persecuted via collaboration with Immigration and Customs Enforcement (ICE), in contrast to counties that provide protections. The analysis includes a difference-in-difference & two-way fixed effect model. Results are robust to nearest-neighbor matching, after the random assignment of treatment, after running estimations using different cutoffs for immigration policies, and with a regression discontinuity model comparing bordering counties with opposite policies. Results are also robust after restricting the data to a single-year policy adoption, using the Sun and Abraham estimator, and with event-study estimation to deal with the staggered treatment issue. In addition, the study reverses the estimation to understand what drives the decision to choose policies to detect the presence of reverse causality biases in the estimated policy impact on economic factors. The evidence demonstrates that providing protections to undocumented immigrants increases economic activity. The estimates show gains in per capita income ranging from 3.1 to 7.2, median wages between 1.7 to 2.6, and GDP between 2.4 to 4.1 percent. Regarding labor, sanctuary counties saw increases in total employment between 2.3 to 4 percent, and the unemployment rate declined from 12 to 17 percent. The data further shows that ICE policies have no statistically significant effects on income, median wages, or GDP but adverse effects on total employment, with declines from 1 to 2 percent, mostly in rural counties, and an increase in unemployment of around 7 percent in urban counties. In addition, results show a decline in the foreign-born population in ICE counties but no changes in sanctuary counties. The study also finds similar results for sanctuary counties when separating the data between urban, rural, educational attainment, gender, ethnic groups, economic quintiles, and the number of business establishments. The takeaway from this study is that institutional inclusion creates the dynamic nature of an economy, as inclusion allows for economic expansion due to the extension of fundamental freedoms to newcomers. Inclusive policies show positive effects on economic outcomes with no evident increase in population. To make sense of these results, the hypothesis and theoretical model propose that inclusive immigration policies play an essential role in conditioning the effect of immigration by decreasing uncertainties and constraints for immigrants' interaction in their communities, decreasing the cost from fear of deportation or the constant fear of criminalization and optimize their human capital.

Keywords: inclusive and exclusive institutions, post matching, fixed effect, time trend, regression discontinuity, difference-in-difference, randomization inference and sun, Abraham estimator

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Authors: Kostandinos Katsamangas, Fawad Inam

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Graphene was dispersed using a tip sonicator and the effect of surfactants were analysed. Sodium Dodecyl Sulphate (SDS) and Polyvinyl Alcohol (PVA) were compared to observe whether or not they had any effect on any de-wrinkling, and secondly whether they aided to achieve better dispersions. There is a huge demand for wrinkle free graphene as this will greatly increase its usefulness in various engineering applications. A comprehensive literature on de-wrinkling graphene has been discussed. Low magnification Scanning Electronic Microscopy (SEM) was conducted to assess the quality of graphene de-wrinkling. The utilization of the PVA has a significant effect on de-wrinkling whereas SDS had minimal effect on the de-wrinkling of graphene.

Keywords: Graphene, de-wrinkling, dispersion, surfactants, scanning electronic microscopy

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Presto Tri Sambodo, Suharjana, Galih Yoga Santiko

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Having an ideal body shape healthy body are the desire of everyone, both young and old. The purpose of this study was to determine: (1) the effect of block circuit training on aerobic fitness and body fat percentage, (2) the effect of non-block circuit training on aerobic fitness and body fat percentage, and (3) differences in the effect of exercise on block and non-circuit training block against aerobic fitness and body fat percentage. This research is an experimental research with the prestest posttest design Two groups design. The population in this study were 57 members of fat loss at GOR UNY Fitness Center. The retrieval technique uses purposive random sampling with a sample of 20 people. The instruments with rockport test (1.6 KM) and body fat percentage with a scale of bioelectrical impedance analysis omron (BIA). So it can be concluded the circuit training between block and non-block has a significant effect on aerobic fitness and body fat percentage. And for differences in the effect of circuit training between blocks and non-blocks, it is more influential on aerobic fitness than the percentage of body fat.

Keywords: circuit training, aerobic fitness, body fat percentage, healthy body

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Authors: Ayyaz Hussain, Tariq Sadad

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Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: K. A. Razhiyah

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Disruptive behavior is a problem that is usually associated with those with autism. There is a need to overcome this behavioral problem because the negative impact of this problem does not only effect the social relation of the students but also can cause uneasiness to those around them. This condition will be worse if the techniques used failed to motivate students to change the behaviour. The purpose of this study was to determine the effect of the circus games technique on the disruptive behavior amongst students with autism. The positive results of the intervention that was carried out for three months show the reduction in disruptive behaviour, and also improvement in the turn-taking and focusing ability aspect. Positive changes shown by the students had an encouraging effect and in a way are helping them in the teaching and learning process.

Keywords: autism, desruptive behaviour, circus, effect

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Authors: Farah Wahida Binti Mohamad Said

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Islamic culture is the way of life a Muslim is defined by the Qur’an and Sunnah. On the other hand, Western culture is fashioned by a host of people; Capitalists, atheists, people who believe in same-gender marriages and others of a similar nature. The main issue that faced by the Muslim in Malaysia is the effect of western culture on Muslim lifestyle. This is because of the influence from western culture that dominates mind of the Muslim and also impressed on their lifestyle. Practically, majority all things have connected with western culture. However, the main objective for this project is to develop the effect of western culture on Muslim lifestyle. This project also focuses on a few aspects that relate with cultural of Muslim and western culture nowadays. This paper will include a few method .The methods for this project are a video, interview etc. Another methodology we will put on next paper for more detail information. As a result, this research found that western cultural will be effect on Muslim lifestyle.

Keywords: effect of western culture, Muslim lifestyle, western culture, western and Muslim culture

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Authors: Matineh Sadat Hosseini Gheidari, Vahid Reza Yazdanpanah

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In this paper, we used the empirical tight-binding method (ETBM) with sp3s* approximation and considering the first nearest neighbor with spin-orbit interactions in order to model superlattice structure (SLS) of (AlₓGa₁₋ₓAs)ₘ/(GaAs)ₙ grown on GaAs (100) substrate at 300K. In the next step, we investigated the behavior of the energy gap and wavelength of this superlattice in terms of different thicknesses of core materials and Al mole fractions. As a result of this survey, we found out that as the Al composition increases, the energy gap of this superlattice has an upward trend and ranges from 1.42-1.63 eV. Also, according to the wavelength range that we gained from this superlattice in different Al mole fractions and various thicknesses, we can find a suitable semiconductor for a special light-emitting diode (LED) application.

Keywords: energy gap, empirical tight-binding method, light-emitting diode, superlattice, wavelength

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Authors: Debalkie Demissie Addisu, Ejigu Alemu Abebe, Tsegay Tensay Assefa

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This study examines the indirect effect of servant leadership on organizational citizenship behavior through perceptions of organizational politics moderated by political skill. This study reports the responses of 321 respondents from six federal public service organizations in Ethiopia. A multi-stage random sampling procedure was employed to select the sampled federal public service organizations. To test hypotheses, the study employed structural equation modeling using AMOS version-26 software. The result revealed that all direct effects have a significant effect. Specifically, servant leadership has a positive effect on organizational citizenship behavior. Likewise, servant leadership has a negative effect on perceptions of organizational politics. Also, a perception of organizational politics has a negative effect on organizational citizenship behavior. Moreover, perceptions of organizational politics competitively mediated the effect of servant leadership on organizational citizenship behavior. As well, political skill moderated the effect of perceptions of organizational politics on organizational citizenship behavior but not the indirect effect. To the best of our knowledge, no one else employs perceptions of organizational politics as a mediating effect between servant leadership and organizational citizenship behavior. Furthermore, we are not aware of anyone else employing political skill as a moderating role in the indirect effect of servant leadership on organizational citizenship behavior through perceptions of organizational politics.

Keywords: servant leadership, organizational citizenship behavior, perceptions of organizational politics, political skill, public service organization, Ethiopia

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Authors: Iyad M. Muslih, Yehya Abdellatif

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In general, shading will reduce the electrical power produced from PV modules and arrays in locations where shading is unavoidable or caused by dynamic moving parts. This reduction is based on the shade effect on the I-V curve of the PV module or array and how the DC/AC inverter can search and control the optimum value of power from this module or array configuration. This is a very complicated task due to different patterns of shaded PV modules and arrays. One solution presented by the inverter industry is to perform the maximum power point tracking (MPPT) at the module level rather than the series string level. This solution is supposed to reduce the shade effect on the total harvested energy. However, this isn’t necessarily the best solution to reduce the shade effect as will be shown in this study.

Keywords: photovoltaic, shade effect, I-V curve, MPPT

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Authors: Abdulla D. Alblooshi

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The stock market is a representation of human behavior and psychology, such as fear, greed, and discipline. Those are manifested in the form of price movements during the trading sessions. Therefore, predicting the stock movement and prices is a challenging effort. However, those trading sessions produce a large amount of data that can be utilized to train an AI agent for the purpose of predicting the stock movement. Predicting the stock market price action will be advantageous. In this paper, the stock movement data of three DFM listed stocks are studied using historical price movements and technical indicators value and used to train an agent using KNN and SVM methods to predict the future price movement. MATLAB Toolbox and a simple script is written to process and classify the information and output the prediction. It will also compare the different learning methods and parameters s using metrics like RMSE, MAE, and R².

Keywords: KNN, ANN, style, SVM, stocks, technical indicators, RSI, MACD, moving averages, RMSE, MAE

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Authors: Musbah Mahfoud, Ibtisam Mustafa

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The present work highlights some of our up-to-date findings on the effect of vanadium addition on the mechanical properties and microstructure of one of the most versatile aluminum-silicon alloys, i.e., A319. In terms of microstructure, it was found that in addition to its ability to refine some of the constituent phases, vanadium also helps in retarding the formation of some of the detrimental intermetallic compounds, such as those involving Al-Fe-Si. Preliminary studies of the effect of vanadium on the mechanical properties of A319 have shown that vanadium additions up to 0.4% cause slight increase in the yield and tensile strength. However, the vanadium addition did not show a significant effect on the hardness of the alloy.

Keywords: aluminium, vanadium, intermetallic, microstructure, mechanical properties

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Authors: Yanshuang Zhang, Byungho Jeong

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In many cases, there is a time lag between the consumption of inputs and the production of outputs. This time lag effect should be considered in evaluating the performance of organizations. Recently, a couple of DEA models were developed for considering time lag effect in efficiency evaluation of research activities. Multi-periods input(MpI) and Multi-periods output(MpO) models are integrated models to calculate simple efficiency considering time lag effect. However, these models can’t discriminate efficient DMUs because of the nature of basic DEA model in which efficiency scores are limited to ‘1’. That is, efficient DMUs can’t be discriminated because their efficiency scores are same. Thus, this paper suggests a super-efficiency model for efficiency evaluation under the consideration of time lag effect based on the MpO model. A case example using a long-term research project is given to compare the suggested model with the MpO model.

Keywords: DEA, super-efficiency, time lag, multi-periods input

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Authors: Niaz Gharavi, Hexin Zhang, Yanjun Xie

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The timber beam end effect in the torsion test is evaluated using binocular stereo vision system. It is recommended by BS EN 408:2010+A1:2012 to exclude a distance of two to three times of cross-sectional thickness (b) from ends to avoid the end effect; whereas, this study indicates that this distance is not sufficiently far enough to remove this effect in slender cross-sections. The shear modulus of six timber beams with different aspect ratios is determined at the various angles and cross-sections. The result of this experiment shows that the end affected span of each specimen varies depending on their aspect ratios. It is concluded that by increasing the aspect ratio this span will increase. However, by increasing the distance from the ends to the values greater than 6b, the shear modulus trend becomes constant and end effect will be negligible. Moreover, it is concluded that end affected span is preferred to be depth-dependent rather than thickness-dependant.

Keywords: end clamp effect, full-size timber test, shear properties, torsion test, wood engineering

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Authors: Ahmad S. Mohd Noor, Emma A. Sirajudin, Nur F. Mat Zain

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Reliability is an important property for knowledge repository system. National Marine Bioinformatics System or NABTICS is a marine knowledge repository portal aimed to provide a baseline for marine biodiversity and a tool for researchers and developers. It is intended to be a large and growing online database and also a metadata system for inputs of research analysis. The trends of present large distributed systems such as Cloud computing are the delivery of computing as a service rather than a product. The goal of this research is to make NABTICS a system of greater availability by integrating it with Cloud based Neighbor Replication and Failure Recovery (NRFR). This can be achieved by implementation of NABTICS into distributed environment. As a result, the user can experience minimum downtime while using the system should the server is having a failure. Consequently the online database application is said to be highly available.

Keywords: cloud, availability, distributed system, marine repository, database replication

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Authors: Soungwan Kim

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The present study analyzed the effect of discrimination experience on the life satisfaction in women with severe disabilities and the mediating effect of disability acceptance. In verifying this mediating effect of disability acceptance between discrimination experience and life satisfaction, both discrimination experience and disability acceptance were found to be statistically significant in the first and second phases. Disability acceptance was found to have a mediating effect on the relationship between discrimination experience and life satisfaction. Based on this finding, measures for enhancing the quality of life in individuals with disabilities that experience low levels of life satisfaction were proposed.

Keywords: disability discrimination, disability acceptance, life satisfaction, mediating effect

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Authors: B. M. Md-Zain, N. A. Rahman, M. A. B. Abdul-Latiff, W. M. R. Idris

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We conducted molecular research to portray phylogenetic relationships of Malaysian primates particularly in the genus of Macaca. We have sequenced cytochrome C oxidase subunit I (COI) of mitochondrial DNA of several individuals from M. fascicularis and M. arctoides. PCR amplifications were performed and COI DNA sequences were aligned using ClustalW. Phylogenetic trees were constructed using distance analyses by employing neighbor-joining algorithm (NJ). We managed to sequence 700 bp of COI DNA sequences. The tree topology showed that M. fascicularis did not clump based on phyleogeography division in Peninsular Malaysia. Individuals from Negeri Sembilan merged together with samples from Perak and Penang into one clade. In addition, phylogenetic analyses indicated that M. arctoides was classified into sinica group instead of fascicularis group supported by genetic distance data. COI gene is an effective locus to clarify phylogenetic position of M. arctoides but not in discriminating M. fascicularis population in Peninsular Malaysia.

Keywords: cercopithecine, long-tailed macaque, Macaca fascicularis, Macaca arctoides

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Authors: Mohammad Anwar, Shah Waliullah

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This study investigated panel data regression models. This paper used Bayesian and classical methods to study the impact of institutions on economic growth from data (1990-2014), especially in developing countries. Under the classical and Bayesian methodology, the two-panel data models were estimated, which are common effects and fixed effects. For the Bayesian approach, the prior information is used in this paper, and normal gamma prior is used for the panel data models. The analysis was done through WinBUGS14 software. The estimated results of the study showed that panel data models are valid models in Bayesian methodology. In the Bayesian approach, the effects of all independent variables were positively and significantly affected by the dependent variables. Based on the standard errors of all models, we must say that the fixed effect model is the best model in the Bayesian estimation of panel data models. Also, it was proved that the fixed effect model has the lowest value of standard error, as compared to other models.

Keywords: Bayesian approach, common effect, fixed effect, random effect, Dynamic Random Effect Model

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Temidayo James Aransiola, Vania Ceccato, Marcelo Justus

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This paper investigates the effect of absolute deprivation (proxy unemployment) and relative deprivation (proxy income inequality) on homicide levels in Brazil. A database from the Brazilian Information System about Mortality and Census of the year 2000 and 2010 was used to estimate negative binomial models of homicide levels controlling for socioeconomic, demographic and geographic factors. Findings show that unemployment and income inequality affect homicides levels and that the effect of the former is more pronounced compared to the latter. Moreover, the combination of income inequality and unemployment exacerbates the overall effect of deprivation on homicide levels.

Keywords: deprivation, inequality, interaction, unemployment, violence

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

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Authors: Debasish Nandy

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India’s foreign policy towards all of her neighbor countries is determinate on the basis of multi-dimensional factors. India’s relations with its South Asian neighbor can be classified into three categories. In the first category, there are four countries -Sri Lanka, Bangladesh, Nepal, and Afghanistan- whose bilateral relationships have encompassed cooperation, irritants, problems and crisis at different points in time. With Pakistan, the relationship has been perpetually adversarial. The third category includes Bhutan and Maldives whose relations are marked by friendship and cooperation, free of any bilateral problems. It is needless to say that Jawaharlal Nehru emphasized on friendly relations with the neighboring countries. The subsequent Prime Ministers of India especially I.K. Gujral had advocated in making of peaceful and friendly relations with the subcontinental countries. He had given a unique idea to foster bilateral relations with the neighbors. His idea is known as ‘Gujral Doctrine’. A dramatical change has been witnessed in Indian foreign policy since 1991.In the post-Cold War period, India’s national security has been vehemently threatened by terrorism, which originated from Pakistan-Afghanistan and partly Bangladesh. India has required a cooperative security, which can be made by mutual understanding among the South Asian countries. Additionally, the countries of South Asia need to evolve the concept of ‘Cooperative Security’ to explain the underlying logic of regional cooperation. According to C. Rajamohan, ‘cooperative security could be understood, as policies of governments, which see themselves as former adversaries or potential adversaries to shift from or avoid confrontationist policies.’ A cooperative security essentially reflects a policy of dealing peacefully with conflicts, not merely by abstention from violence or threats but by active engagement in negotiation, a search for practical solutions and with a commitment to preventive measures. Cooperative assumes the existence of a condition in which the two sides possess the military capabilities to harm each other. Establishing cooperative security runs into a complex process building confidence. South Asian nations often engaged with hostility to each other. Extra-regional powers have been influencing their powers in this region since a long time. South Asian nations are busy to purchase military equipment. In spite of weakened economic systems, these states are spending a huge amount of money for their security. India is the big power in this region in every aspect. The big states- small states syndrome is a negative factor in this respect. However, India will have to an initiative to extended ‘track II diplomacy’ or soft diplomacy for its security as well as the security of this region.Confidence building measures could help rejuvenate not only SAARC but also build trust and mutual confidence between India and its neighbors in South Asia. In this paper, I will focus on different aspects of India’s policy towards it, South-Asian neighbors. It will also be searched that how India is dealing with these countries by using a mixed type of diplomacy – both idealistic and realistic points of view. Security and cooperation are two major determinants of India’s foreign policy towards its South Asian neighbors.

Keywords: bilateral, diplomacy, infiltration, terrorism

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Authors: Frank Frimpong Opuni, Michael Mba Allan, Kwame Adu-Gyamfi, Michael Sarkodie Baffoe

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This paper assesses the effect of relationship marketing on service quality and customer satisfaction from the perspective of the moderating role of emotional intelligence in the insurance industry in Ghana. A descriptive quantitative research technique was employed. A random sample of 384 each of customers and service providers in 3 insurance firms in Accra were used as the source of data. According to findings, emotional intelligence makes a strong positive effect on relationship marketing at 5% significance level, r (283) = .817, p = .000. Though relationship marketing makes a strong positive effect on service quality (r = .767, p < .05) and customer satisfaction (r = .647, p < .05), this effect becomes insignificant (p > .05) when the effect of emotional intelligence on relationship marketing is controlled for. It is therefore recommended that insurance firms give priority to equipping their relationship employees with emotional intelligence to maximise service quality and customer satisfaction.

Keywords: relationship marketing, service quality, customer satisfaction, emotional intelligence

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Authors: Ashish Kumar, Kaptan Singh, Amit Saxena

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Today, it is believed that crimes have the greatest impact on a person's ability to progress financially and personally. Identifying places where individuals shouldn't go is crucial for preventing crimes and is one of the key considerations. As society and technologies have advanced significantly, so have crimes and the harm they wreak. When there is a concentration of people in one place and changes happen quickly, it is even harder to prevent. Because of this, many crime prevention strategies have been embraced as a component of the development of smart cities in numerous cities. However, crimes can occur anywhere; all that is required is to identify the pattern of their occurrences, which will help to lower the crime rate. In this paper, an analysis related to crime has been done; information related to crimes is collected from all over India that can be accessed from anywhere. The purpose of this paper is to investigate the relationship between several factors and India's crime rate. The review has covered information related to every state of India and their associated regions of the period going in between 2001- 2014. However various classes of violations have a marginally unique scope over the years.

Keywords: K-nearest neighbor, random forest, decision tree, pre-processing

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Authors: Amira Kaddour

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To curb the problem of liquidity in times of financial crises, two cases arise; the Bailout or Bailin, two opposite choices that elicit the analysis of their effect on moral hazard. This paper attempts to empirically analyze the effect of these two types of events on the behavior of investors. For this end, we use the Emerging Market Bonds Index (EMBI-JP Morgan), and its excess of return, to detect the change in the risk premia through a Markov switching model. The results showed the transition to two types of regime and an effect on moral hazard; Bailout is an incentive of moral hazard, Bailin effectiveness remains subject of credibility.

Keywords: Bailout, Bailin, Moral hazard, financial crisis, Markov switching

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Authors: Shamshiri Mitra, Abedi Rahim

Abstract:

This paper investigates corporate investment behavior and its relationship with ownership structure and financial leverage for the listed company of Tehran stock exchange during 2008-2012. The results show that the concentration of ownership has s significant positive effect on corporate investment. The results for the kind of major owners show that institutional ownership had a positive significant effect and state and individual ownership had negative significant effects on the corporate investment but the effect of corporate ownership was not significant. Furthermore the effect of financial leverage was negative and significant.

Keywords: corporate investment behavior, financial leverage, ownership structure corporate investment behavior

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