2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)最新文献

{"title":"Understanding of Slope of a Straight Line in Communication Context","authors":"Fauziah Abd Kadir, Mohd Rosli A. Hamid","doi":"10.1109/MACS48846.2019.9024802","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024802","url":null,"abstract":"This paper describes the understanding of slope of a straight line among Semester One Science Foundation students in communication context based on radical constructivism. This study uses the purposive sampling method. The data were collected through clinical interview techniques, while the research instrument involved four types of clinical interview protocols. The study found that students' communication of slope of a straight line can be categorized into seven aspects, namely defining slopes, defining positive slopes, defining negative slopes, defining slopes of “−3”, calculating slopes, using linear equations, and drawing graphs.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121791599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Profiling Social Media Campaigns and Political Influence: The Case of Pakistani Politics","authors":"M. Bilal, Nadia Malik, Nauman Bashir, Mohsen Marjani, I. A. Hashem, A. Gani","doi":"10.1109/MACS48846.2019.9024774","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024774","url":null,"abstract":"Social networking websites have been widely used for political awareness and campaigns. With the increasing reliance of political parties and supporters on social media, interesting patterns can be extracted by analyzing social media profiles. The previous approaches perform political analysis used social media data mainly focused on analyzing only textual data for the two-party system. However, the social media engagement, i.e. posts reaction, of political supporters can be used to identify the most influential political party. Therefore, to explore the content of social media political campaigns and to identify the most influential political party using social engagement in a multi-party system, we extracted Facebook data using Facebook Graph API for the three most popular political parties in Pakistan, i.e. PTI, PML-N and, PPP. The results reported that PTI relies more on the English language when compared with PML-N and PTI. However, video content is most prominently used by all three parties. The ratio of reactions on Facebook posts varies, PTI has received the highest number of “LIKE” and “SHARE”, PML-N, and PPP has received the major number of “HAHA” and “SAD”. PTI appears as the most influential party in comparison with PML-N and PPP. The influence score for each party appeared in accordance with the results of Pakistan's General Elections 2018. Hence, the proposed influence score irrespective of party-system can be used by researchers to improve the accuracy of electoral prediction. The insights highlighted by this study can also be used by political parties to improve their social media campaigns for increasing social influence.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129772495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of Knowledge-Based HRM Practices on Productivity of Knowledge Workers: A Study on Malaysian Universities","authors":"Leong Kai Sheng, S. M. H. S. Khairuddin, Shehnaz Tehseen, Yeong Hui Yan","doi":"10.1109/MACS48846.2019.9024787","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024787","url":null,"abstract":"Knowledge workers have contributed significantly to the development of nations. There are various researches that extensively recognized the positive impact of knowledge-based HRM practices on knowledge worker's productivity. While there are studies regarding knowledge-based HRM practices across different countries and industries, however, there is dearth of research regarding the impact of knowledge-based HRM practices on universities' knowledge workers. Therefore, the aim of this current study was to fill up the gap by analysing the influence of various knowledge-based HRM practices on the productivity of knowledge workers in universities. This study investigated HRM practices namely knowledge-based recruiting practices, knowledge-based training and development practices, knowledge-based performance appraisal practices and knowledge-based compensation practices on the quality and quantity of the productivity of knowledge workers. Herzberg's Two-Factor Theory, Expectancy Theory, and MacGregor's Theory X and Theory Y were used as underpinning theories to support the proposed conceptual model. Eight hypotheses were developed based on the proposed research model and standard instrument was used to obtain data. By employing non-probability sampling method, a total of 129 knowledge workers in Selangor and Kuala Lumpur participated in survey. The data were collected at one point of time across the sample population. The data obtained were assessed using SPSS and Partial Least Square Structural Equation Modelling (PLS-SEM). Results revealed the positive and significant influence of knowledge-based recruiting practices and knowledge-based compensation practices on quality and quantity of knowledge workers' productivity. Knowledge-based training and development practices were found to have their positive and significant impact on quantity of knowledge workers' productivity but not on quality of knowledge workers' productivity. Additionally, knowledge-based performance appraisal was not found to have its positive as well as significant impact on quality and quantity of knowledge worker's productivity.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"20 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130565235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent System for Classification of Pulmonary Diseases from Lung Sound","authors":"Syed Zohaib Hassan Naqvi, M. Choudhry, A. Khan, Maheen Shakeel","doi":"10.1109/MACS48846.2019.9024831","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024831","url":null,"abstract":"Lung disease belongs to the class of fatal disease according to World Health Organization statistics. Asthma and bronchitis are most prominent among these abnormalities. Identification of Lung disease from lung sound analysis is still question mark in medicine. In this paper, Asthma and Bronchitis are identified from Lung sound analysis from application of signal processing techniques. Data is acquired from 50 Asthma, 50 Bronchitis and 50 Normal subjects. Empirical mode decomposition method is used at preprocessing stage. Standard deviation, Shannon energy, peak to peak and root mean square features are estimated and system accuracy on different k-NN classifier is analyzed via Matlab 2019a. The system evidenced greater than 99.30% accuracy. Further improvement can be done in exploring new features of Lung sounds for better and robust classification of different Lung diseases.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126371665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spotting Urdu Stop Words By Zipf's Statistical Approach","authors":"Nuzhat Khan, Muhammad Paend Bakht, Muhammad Junaid Khan, Abdul Samad, Gul Sahar","doi":"10.1109/MACS48846.2019.9024817","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024817","url":null,"abstract":"This paper presents innovative method to extract stop words from large Urdu text. Stop words are less meaningful words in natural language that slow down language processing and affect language analysis negatively. For language analysis, stop words are removed first to ensure fast data processing. But for Urdu language, there is no reliable stop words removal method. In this work, we applied Zipf's law of two factors dependency with least effort approach to spot stop words in Urdu language corpus. Urdu corpus is specifically created for this research. All Urdu text processing and investigation is carried out in Python 3. 4. Previous work for stop words removal is also investigated and proved less helpful. By using Zipfian approach, out of 500 high frequency words, 358 words are identified as stop words. It is observed that by only focusing on 0.01% of large corpus, almost all the stop words can be spotted to create a stop words list with least manual effort. Furthermore, statistical patterns in stop words, content words, stop words vs content words ratio in data samples and dependency of stop words and content words over data size is also examined. In terms of data size, frequency and ranks, Zipf's law and Heap's law coexist in Urdu stop words. Stop words tend to follow some predictable and measurable patterns that can lead to reliable probabilistic methods for Urdu processing. This deterministic approach provides a strong research ground to explore stop words in Urdu text statistically.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126542663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Automated System towards Diagnosis of Pneumonia using Pulmonary Auscultations","authors":"Sumair Aziz, Muhammad Umar Khan, Maheen Shakeel, Zohaib Mushtaq, A. Khan","doi":"10.1109/MACS48846.2019.9024789","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024789","url":null,"abstract":"Respiratory sounds carry significant information about the condition of respiratory system. Respiratory sounds are often affected by sounds emanating from heart and other organs thus making the analysis task more complex. Pneumonia is a very common lungs disease and requires efficient diagnosis at initial stage for proper treatment. In this research, an automated system for diagnosis of Pneumonia based on auscultations is proposed. Auscultation signals are first preprocessed through Empirical mode decomposition (EMD), which decomposes original signal into its constituent components known as intrinsic mode functions (IMFs). Preprocessed signal is reconstructed by addition of only those IMFs which carry high discriminative information among healthy and Pneumonia subjects. IMFs which carry redundant and noisy data are rejected thus making preprocessing more effective. Next, characteristic features are extracted by fusion of Mel frequency cepstral coefficients (MFCC) and time domain features. Finally, Support Vector Machines (SVM) classifier is trained and tested through 5-fold cross validation. Experimental evaluation of proposed approach is performed on range of various classifiers on self-collected dataset which contains 480 auscultation signals of normal and Pneumonia subjects. SVM with Quadratic kernel achieved best classification results in terms of accuracy of 99.7%.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132524045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT based Smart Security System using PIR and Microwave Sensors","authors":"M. Saeed, Raja Raheel Ahmed, Omar Bin Samin, Nusrat Ali","doi":"10.1109/MACS48846.2019.9024813","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024813","url":null,"abstract":"The Internet of Things (IoT) helps to create safe cities, businesses, and homes by allowing both public and private organizations to remotely and securely monitor facilities and public spaces in real-time with smart surveillance and security solutions. In this paper, an economical, secure, fault-tolerant and easy to install/use surveillance system has been proposed. A couple of PIR and microwave sensors are used to detect heat signatures. These sensor nodes are physically connected using Arduino Mega Board. If intrusion is detected, the Arduino Mega Board generates an alert notification and sends it to the 3G/GPRS Shield (SIM5215A) module. The first connection is made with the online server using 3G/GPRS while the second connection is made with the user's cell phone using GSM. The proposed system has been validated on a variety of test cases and produced optimal results.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133066737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Objective Optimization Techniques for Software Refactoring: A Systematic Literature Review","authors":"Muhammad Zaid Rafique, K. Alam, Umer Iqbal","doi":"10.1109/MACS48846.2019.9024773","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024773","url":null,"abstract":"Software Refactoring is an essential activity of software maintenance. It aims at improving the internal structure of the program without affecting its external functionalities which not only aids in improving maintainability and readability but also helps in reducing overall software complexity. Many different manuals and automated software refactoring tools are available but most of these tools focus single objective refactoring i.e. improving the quality or reducing the code lines. Software refactoring involves many factors so different authors have proposed different multi-objective software refactoring approaches. We have performed systematic literature to classify and analyzed the studies published in the field of multi-objective software refactoring. The main objectives of our research are to categorize the studies on multi-objective software refactoring according to 4 criteria. We have considered studies from electronics databases from 2014 to 2019. A total of 19 studies were finalized based on our inclusion-exclusion and quality assessment criteria. The results of our research show that NSGA-II is a widely popular technique in the domain of multi-objective software refactoring whereas NSGA-III is popular when many objectives were considered. Furthermore, 11 most widely uses open source and industrial projects are identified which are used to evaluate the multi-objective software refactoring approaches. It was also observed that Precision, Recall and Inverse Generation Distance are commonly used evaluation metrics. The chronological distribution of studies shows that 2016 was the most productive research year in this field. Our results show that 76% of studies are ranked high based on our predefined quality assessment criteria. Based on our results we have concluded that multiobjective software refactoring is still an emerging field and there is a need to apply the latest state-of-the-art multi-objective approaches to get better results.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133185186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bitcoin price prediction using Deep Learning Algorithm","authors":"M. Rizwan, Sanam Narejo, Moazzam Javed","doi":"10.1109/MACS48846.2019.9024772","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024772","url":null,"abstract":"The world has more than 5000 digital-currencies, bitcoin is one of it, which has more than 5.8 million dynamic client and approximately more than 111 exchanges throughout the world. So, the aim for this paper is to do the near prediction of the price of Bitcoin in USD. Precious details are taken from the price index of Bitcoin. A Bayesian recurrent hierarchical (RNN) neural network and a long-term memory (LSTM) network can accomplish this function. The total identification accuracy of 52% and an 8% RMSE is obtained by the LSTM. In contrast to the profound training systems, the common ARIMA method for the prediction of time series. This model have not much efficient as deep learning model can be performed. The deep learning methods were predicted to outperform the poorly performing ARIMA prediction. So here we used Gated Recurrent Network model (GRU) to forecasting Bitcoin price Eventually, all deep learning models have a GPU and CPU that beat the GPU implemented by 94.70 percent for their GPU training time.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117114918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmenting melanoma Lesion using Single Shot Detector (SSD) and Level Set Segmentation Technique","authors":"Faaiza Rashid, Aun Irtaza, Nudrat Nida, A. Javed, H. Malik, K. Malik","doi":"10.1109/MACS48846.2019.9024823","DOIUrl":"https://doi.org/10.1109/MACS48846.2019.9024823","url":null,"abstract":"Melanoma is a lethal type of skin cancer that orginates fron melanocytes cells of skin and it is responsible of several deaths annually due to exposure of ultraviolet radiations. Early diagnosis and proper treatment of melanoma significantly improves the patient's survival rate. In the computer aided diagnosis, the automatic segmentation is first step in early and accurate diagnosis of the Melanoma lesion area. However, the presence of natural or clinical artifacts hinders the precise lesion segmentation. The goal of our work is to establish a novel pipeline that automatically pre-process, localize and then segment the melanoma lesion precisely and improve its segmentation accuracy. In our proposed method, dermoscopic images are segmented in three steps: 1. Preprocessing using morphological operations to remove hair. 2. Localization of melanoma lesion by utilizing a deep convolutional neural network named as Single-Shot Detection (SSD) network, 3. Segmentation using level set algorithm. The proposed approach was evaluated on ISBI 2016 challenge dataset (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). On ISIC 2016, our method achieved an average of Jc, Di and Ac as 0.82, 0.901 and 0.90 respectively. The results of the segmentation are also compared with the state-of-the-art methods to justify the effectiveness of the proposed approach.","PeriodicalId":434612,"journal":{"name":"2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125688741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}