Pub Date : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502208
M. Usman Ashraf
This paper provides an overview of the evolving field of emotion detection and identifies the current generation of methods of emotion detection from social media platforms as well as the challenges. The challenges in the field of current emotion detection are discussed in detail and potential alternatives are proposed to enhance the ability to detect emotions in real-life systems that emphasize interactions between humans and computers as well as advertisements, recommendation systems, and medical fields such as computer-based therapy. These solutions include the extraction of semantic analysis keywords, and ontology design with the evaluation of emotions. There are multiple models and classifications of emotions such as Ekman’s model (Happy, Anger, Sad, Disgust,Fear, Surprise), and Plutchik’s model (anger-fear, surprise-anticipation, joy-sadness, joy-sadness). Further, a systematic review of publications on textual emotions detection from social media platforms, state-of-the-art methods, and existing challenges presented. Finally, we conclude with some recommendations based on critical analysis of existing techniques and determine future research directions presented at last.
{"title":"A Survey on Emotion Detection from Text in Social Media Platforms","authors":"M. Usman Ashraf","doi":"10.54692/lgurjcsit.2021.0502208","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502208","url":null,"abstract":"This paper provides an overview of the evolving field of emotion detection and identifies the current generation of methods of emotion detection from social media platforms as well as the challenges. The challenges in the field of current emotion detection are discussed in detail and potential alternatives are proposed to enhance the ability to detect emotions in real-life systems that emphasize interactions between humans and computers as well as advertisements, recommendation systems, and medical fields such as computer-based therapy. These solutions include the extraction of semantic analysis keywords, and ontology design with the evaluation of emotions. There are multiple models and classifications of emotions such as Ekman’s model (Happy, Anger, Sad, Disgust,Fear, Surprise), and Plutchik’s model (anger-fear, surprise-anticipation, joy-sadness, joy-sadness). Further, a systematic review of publications on textual emotions detection from social media platforms, state-of-the-art methods, and existing challenges presented. Finally, we conclude with some recommendations based on critical analysis of existing techniques and determine future research directions presented at last.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125105116","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}
Pub Date : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502203
Shahzad Ahmed Khan
Recommender systems help humans in filtering and finding the right information from the enormous amount of data. Hostels are more famous than hotels for solo travelers, but no prior research related to recommender systems has been conducted in this domain. Hostels allow users to provide multi-criteria ratings and traditional recommender systems are not able to provide effective recommendations in case of multi-dimensionality i.e. contextual information and multi-criteriaratings. So, we have proposed a novel hybrid recommender system (SAFCHERS) that chooses the hostel's features for computation dynamically and provides explainable and better recommendations than the traditional recommender systems.
{"title":"A Novel Model for Explainable Hostel Recommender System Using Hybrid Filtering","authors":"Shahzad Ahmed Khan","doi":"10.54692/lgurjcsit.2021.0502203","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502203","url":null,"abstract":"Recommender systems help humans in filtering and finding the right information from the enormous amount of data. Hostels are more famous than hotels for solo travelers, but no prior research related to recommender systems has been conducted in this domain. Hostels allow users to provide multi-criteria ratings and traditional recommender systems are not able to provide effective recommendations in case of multi-dimensionality i.e. contextual information and multi-criteriaratings. So, we have proposed a novel hybrid recommender system (SAFCHERS) that chooses the hostel's features for computation dynamically and provides explainable and better recommendations than the traditional recommender systems.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114176855","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}
Pub Date : 2021-06-21DOI: 10.54692/lgurjcsit.2021.0502204
Zubair Akbar
The pandemic of Covid-19 which started in the year 2019 did not just cause an effect on the living of millions of people but in the economic and social sectors of every part of the world as well. It is a challenging task to determine the interrelation between COVID-19 cases concerning the economy in the top affected countries. This paper explores; how severe Impact of COVID-19 1st wave on the economic facets of Pakistan as compared to the Top Fifteen affected countries. Moreover, this paper uses COVID-19 well-known dataset provided by John Hopkins and Stock Market Datasets collectively to carry out the critical analysis successfully. We found a relationship between the cumulative numbers of confirmed cases in each country with a declining state of countries' economies: the higher decline in the stock market indicates a higher number of confirmed cases.
{"title":"Using Data Mining Technique to Measure the Impact of COVID-19: 1st Wave on the Stock Market of Top Fifteen Affected Countries","authors":"Zubair Akbar","doi":"10.54692/lgurjcsit.2021.0502204","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0502204","url":null,"abstract":"The pandemic of Covid-19 which started in the year 2019 did not just cause an effect on the living of millions of people but in the economic and social sectors of every part of the world as well. It is a challenging task to determine the interrelation between COVID-19 cases concerning the economy in the top affected countries. This paper explores; how severe Impact of COVID-19 1st wave on the economic facets of Pakistan as compared to the Top Fifteen affected countries. Moreover, this paper uses COVID-19 well-known dataset provided by John Hopkins and Stock Market Datasets collectively to carry out the critical analysis successfully. We found a relationship between the cumulative numbers of confirmed cases in each country with a declining state of countries' economies: the higher decline in the stock market indicates a higher number of confirmed cases.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128820751","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501174
Ehsan Elahi
Named data networking (NDN) approach has natural benefits within Mobile Ad hoc Network (MANET) but presents different issues as well. Space for cache, energy, and mobility of devices in a MANET is limited; therefore, we need for an enhanced judgement concerning which data to be store and where to be cache. A Time dependent Popularity Caching Scheme (TDPC) has suggested which selects nodes for caching the content on the forwarding path of packet and chooses the contents which have cached constructed on their time dependent popularity. At this interval, the cache distribution of the content and the storage capability of the devices are also measured. Results of the suggested TDPC approach are evaluated by using the simulator ndnSIM which is beached on Network Simulator 3 (NS-3). Simulation outcomes show that TDPC has good performance in expression of cache hit ratio, content retrieval interval, total cache copies and compared to the Dynamic Caching Strategy for CCN-based MANETs (CSCM). The goal of TDPC is to reduce cache redundancy, retrieval time of content and total number of cache copies.
{"title":"Time Dependent Popularity Caching Scheme for NDN Based MANETs","authors":"Ehsan Elahi","doi":"10.54692/lgurjcsit.2021.0501174","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501174","url":null,"abstract":"Named data networking (NDN) approach has natural benefits within Mobile Ad hoc Network (MANET) but presents different issues as well. Space for cache, energy, and mobility of devices in a MANET is limited; therefore, we need for an enhanced judgement concerning which data to be store and where to be cache. A Time dependent Popularity Caching Scheme (TDPC) has suggested which selects nodes for caching the content on the forwarding path of packet and chooses the contents which have cached constructed on their time dependent popularity. At this interval, the cache distribution of the content and the storage capability of the devices are also measured. Results of the suggested TDPC approach are evaluated by using the simulator ndnSIM which is beached on Network Simulator 3 (NS-3). Simulation outcomes show that TDPC has good performance in expression of cache hit ratio, content retrieval interval, total cache copies and compared to the Dynamic Caching Strategy for CCN-based MANETs (CSCM). The goal of TDPC is to reduce cache redundancy, retrieval time of content and total number of cache copies.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115600643","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501172
Aftab Ahmad Malik
During Covid-19 spread over, throughout the world, online classes were arranged at school and University level. In certain institutions, the experiments of online systems were successful while in certain cases difficulties were observed and reported. In this paper, we are interested to highlight and analyze the problems occurring in assessment and evaluation. The delivery of lectures and grades assessment, comparison of time spent on in-class lectures and online, the provided infrastructure and technology to faculty and its synchronization with that arranged by students. The objective type questions are automatically marked by the system for example Moodle, whereas the essay type (subjective type) questions are normally preferred to be marked by faculty on the system. This is indeed a facility that all students are provided with different question papers. The online system is more economical, time-saving and easily usable. The most serious issues have been unfair means and cheating used by students during examinations. A detailed analysis of the state-of-the-art is presented in this paper. We also present a comparison of online and in-class teaching and assessment, but irresistibly the benefits of online system are more advantageous. For example, a lot of stationary is saved. The online system causes breaks and pauses during delivery of lessons due to instability of internet and concavity issues. The most important is the choice concerning system subject to available facility with leaner and the faculty and its compatibility with emphasis on technology. The use of anti-cheating software makes the examination secure.
{"title":"Analysis of Advantages and Problems in Teaching and Assessment with Online System during Covid-19","authors":"Aftab Ahmad Malik","doi":"10.54692/lgurjcsit.2021.0501172","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501172","url":null,"abstract":"During Covid-19 spread over, throughout the world, online classes were arranged at school and University level. In certain institutions, the experiments of online systems were successful while in certain cases difficulties were observed and reported. In this paper, we are interested to highlight and analyze the problems occurring in assessment and evaluation. The delivery of lectures and grades assessment, comparison of time spent on in-class lectures and online, the provided infrastructure and technology to faculty and its synchronization with that arranged by students. The objective type questions are automatically marked by the system for example Moodle, whereas the essay type (subjective type) questions are normally preferred to be marked by faculty on the system. This is indeed a facility that all students are provided with different question papers. The online system is more economical, time-saving and easily usable. The most serious issues have been unfair means and cheating used by students during examinations. A detailed analysis of the state-of-the-art is presented in this paper. We also present a comparison of online and in-class teaching and assessment, but irresistibly the benefits of online system are more advantageous. For example, a lot of stationary is saved. The online system causes breaks and pauses during delivery of lessons due to instability of internet and concavity issues. The most important is the choice concerning system subject to available facility with leaner and the faculty and its compatibility with emphasis on technology. The use of anti-cheating software makes the examination secure.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117104292","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501177
Salman Qadri
The world is moving towards cost-effective and time-constrained solutions. The uses of applications and automated devices have been growing day by day. In computing, resources available in personal computers are limited due to less storage capacity and lower computation speeds. Using all applications on personal systems may not be cost-effective. Therefore, the trends of online storage and computing have become popular. On the other hand, there must be some serving end for these users. One of the major issues, due to the growth of data centers is the increase in power usage of a larger number of servers and network devices. These devices are power-hungry and consume energy even during idle hours even if there are no network traffic loads. The cost of energy used and dissipated is increased in this situation. In this paper, we have given a solution for efficient usage of energy efficiency in data center networks based on traffic loads. We have proposed a model to use traffic intensity to decide the number of machines inactive conditions so that we can save the energy consumption in data center networks. We have implemented this proposed model and simulated it to validate it.
{"title":"Traffic Intensity Based Energy Efficiency Architecture for Data-Centers","authors":"Salman Qadri","doi":"10.54692/lgurjcsit.2021.0501177","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501177","url":null,"abstract":"The world is moving towards cost-effective and time-constrained solutions. The uses of applications and automated devices have been growing day by day. In computing, resources available in personal computers are limited due to less storage capacity and lower computation speeds. Using all applications on personal systems may not be cost-effective. Therefore, the trends of online storage and computing have become popular. On the other hand, there must be some serving end for these users. One of the major issues, due to the growth of data centers is the increase in power usage of a larger number of servers and network devices. These devices are power-hungry and consume energy even during idle hours even if there are no network traffic loads. The cost of energy used and dissipated is increased in this situation. In this paper, we have given a solution for efficient usage of energy efficiency in data center networks based on traffic loads. We have proposed a model to use traffic intensity to decide the number of machines inactive conditions so that we can save the energy consumption in data center networks. We have implemented this proposed model and simulated it to validate it.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133215268","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501173
Salman Qadri
In this study, we describe a technique that used a machine learning (ML) approach to classify four (4) different cotton leaf varieties namely; BS-15, S-32, Z-31, and Z-32. Each variety of cotton leaves were collected from 500 Farmers. These image datasets are captured by using the cell phone camera in the open agricultural field area, and every image was captured from both sides (Front and Back) of the cotton leaf. Each variety of cotton has used over 300 (150 Front Side and 150 Back Side of the leaves) leaf images and the total calculated cotton leaves are 1200 (300 x 4) as leaf image samples. These sample datasets have analyzed through image preprocessing and image segmentation process. Each image was employing four different non-over-lapping regions of interest (ROI’s) and calculated a total of 4800 (1200 x 4) ROI’s. The acquired datasets are employed different machine learning features such as Scalability, Texture, Spectral, Binary, Histogram, Rotational, and translational (R-S-T). A total of fifty-seven (57) machine learning features were evaluated on each ROI and a total calculated 273,600 (4800 x 57) features. Furthermore, the Correlation-Based Feature Selection (CFS) genetic algorithm technique was employed for feature optimization. It has been evaluated 22 optimized features and applying different machine learning (M-L) classifiers namely; K-Nearest Neighbor (K-NN), K*, Random Forest (RF) Tree, and Naive Bayes (NB) Tree. The resulting accuracy produced by K-NN presented is 98.9167% on (512 x 512) ROI’s. The individually overall result accuracy dataset values by using K-NN classifier on the four varieties of cotton leaf namely; BS-15, S-32, Z-31, and Z-32 were evaluated 97.83%, 99.50%, 99%, and 99.33%, respectively.
在本研究中，我们描述了一种使用机器学习(ML)方法对四(4)种不同的棉花叶片品种进行分类的技术，即;BS-15, S-32, Z-31和Z-32。每个品种的棉花叶子都是从500个农民那里收集来的。这些图像数据集是利用手机相机在开阔的农田地区拍摄的，每张图像都是从棉花叶片的正面和背面拍摄的。每个棉花品种使用了300多个叶片图像(正面150个，背面150个)，计算出的棉花叶片总数为1200个(300 × 4)作为叶片图像样本。这些样本数据集通过图像预处理和图像分割过程进行分析。每张图像采用四个不同的非重叠感兴趣区域(ROI 's)，并计算出总共4800 (1200 x 4)个ROI 's。获取的数据集采用不同的机器学习特征，如可伸缩性、纹理、光谱、二进制、直方图、旋转和平移(R-S-T)。在每个ROI上总共评估了57个机器学习特征，总共计算了273600个(4800 x 57)特征。在此基础上，采用基于关联的特征选择(CFS)遗传算法进行特征优化。它已经评估了22个优化的特征，并应用了不同的机器学习(M-L)分类器，即;K-最近邻(K- nn)、K*、随机森林(RF)树和朴素贝叶斯(NB)树。在(512 × 512) ROI上，K-NN产生的准确率为98.9167%。K-NN分类器在4种棉花叶片上的单独总体结果精度数据集值为;BS-15、S-32、Z-31和Z-32的评价分别为97.83%、99.50%、99%和99.33%。
{"title":"A Spatial Model of K-Nearest Neighbors for Classification of Cotton (Gossypium) Varieties based on Image Segmentation","authors":"Salman Qadri","doi":"10.54692/lgurjcsit.2021.0501173","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501173","url":null,"abstract":"In this study, we describe a technique that used a machine learning (ML) approach to classify four (4) different cotton leaf varieties namely; BS-15, S-32, Z-31, and Z-32. Each variety of cotton leaves were collected from 500 Farmers. These image datasets are captured by using the cell phone camera in the open agricultural field area, and every image was captured from both sides (Front and Back) of the cotton leaf. Each variety of cotton has used over 300 (150 Front Side and 150 Back Side of the leaves) leaf images and the total calculated cotton leaves are 1200 (300 x 4) as leaf image samples. These sample datasets have analyzed through image preprocessing and image segmentation process. Each image was employing four different non-over-lapping regions of interest (ROI’s) and calculated a total of 4800 (1200 x 4) ROI’s. The acquired datasets are employed different machine learning features such as Scalability, Texture, Spectral, Binary, Histogram, Rotational, and translational (R-S-T). A total of fifty-seven (57) machine learning features were evaluated on each ROI and a total calculated 273,600 (4800 x 57) features. Furthermore, the Correlation-Based Feature Selection (CFS) genetic algorithm technique was employed for feature optimization. It has been evaluated 22 optimized features and applying different machine learning (M-L) classifiers namely; K-Nearest Neighbor (K-NN), K*, Random Forest (RF) Tree, and Naive Bayes (NB) Tree. The resulting accuracy produced by K-NN presented is 98.9167% on (512 x 512) ROI’s. The individually overall result accuracy dataset values by using K-NN classifier on the four varieties of cotton leaf namely; BS-15, S-32, Z-31, and Z-32 were evaluated 97.83%, 99.50%, 99%, and 99.33%, respectively.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134222911","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501171
Muhammad Amjad Khan
Cloud computing is getting involved in almost every technological field to serve customers in a more efficient way. The shared resources (pool) with different configurations according to the user’s needs are provided by cloud vendors. Users stores their data on the cloud, data can be personal, or organizational, and data of every type must be secure on clouds. Making the data secure and reducing the integrity of data during transfer through public channels. In this paper, we will try to make data secure using image steganography. Using the steganography technique, we use encryption-decryption of data into images and make data invisible.
{"title":"Information Security for Cloud using Image Steganography","authors":"Muhammad Amjad Khan","doi":"10.54692/lgurjcsit.2021.0501171","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501171","url":null,"abstract":"Cloud computing is getting involved in almost every technological field to serve customers in a more efficient way. The shared resources (pool) with different configurations according to the user’s needs are provided by cloud vendors. Users stores their data on the cloud, data can be personal, or organizational, and data of every type must be secure on clouds. Making the data secure and reducing the integrity of data during transfer through public channels. In this paper, we will try to make data secure using image steganography. Using the steganography technique, we use encryption-decryption of data into images and make data invisible.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133784575","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501176
Muhammad Khawar Bashir
Today the rapid proliferation of the internet provides an environment where efficient e-commerce solutions can be developed. The electronic market is gaining more attention in the global economy, it gives buyers and sellers more liberty to trade cost-effectively and allows access to an adequate amount of data for analysis. New trading agents have been developed for the best utilization of such data. These agents design strategies using financial analysis techniques such as technical indicators. Two very well-known technical indicators used to develop strategies are Convergence-Divergence (MACD) and Stochastic Oscillator (SO). This paper aims to devise a trading algorithm that combines MACD and SO in a single strategy and check the reliability of the combined signals it generates. JTAP simulation system has been used to test the proposed strategy. In this paper, we evaluated the performance of our proposed strategy when implemented on shares of Karachi Stock Exchange, Pakistan which proves improvement of strategy.
{"title":"Trading Algorithm Model Based on Technical Indicators","authors":"Muhammad Khawar Bashir","doi":"10.54692/lgurjcsit.2021.0501176","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501176","url":null,"abstract":"Today the rapid proliferation of the internet provides an environment where efficient e-commerce solutions can be developed. The electronic market is gaining more attention in the global economy, it gives buyers and sellers more liberty to trade cost-effectively and allows access to an adequate amount of data for analysis. New trading agents have been developed for the best utilization of such data. These agents design strategies using financial analysis techniques such as technical indicators. Two very well-known technical indicators used to develop strategies are Convergence-Divergence (MACD) and Stochastic Oscillator (SO). This paper aims to devise a trading algorithm that combines MACD and SO in a single strategy and check the reliability of the combined signals it generates. JTAP simulation system has been used to test the proposed strategy. In this paper, we evaluated the performance of our proposed strategy when implemented on shares of Karachi Stock Exchange, Pakistan which proves improvement of strategy.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128207339","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}
Pub Date : 2021-02-09DOI: 10.54692/lgurjcsit.2021.0501178
Muhammad Waseem
To make dictionaries complete and to keep their size restricted, there is an approach in the linguistic world to equip these dictionaries with morphological information. This module of morphological information is usually known as a morphological analyzer or morphological classifier, which normally contains the complete possible linguistic information about each word for that particular language and it also describes the rules of derivations from the root of a word and its various inflections, respectively. In this work, a classifier for Urdu verbs (CUV) is proposed which is still a challenging research issue, as Urdu is a language of high inflection and derivation. The available stemmers for Urdu do not provide enough information about inflectional and derivational forms of words. Also, morphological classifiers available for Urdu are not worthy of handling various problems and delivering results that prune errors. In our work, a rule based CUV is designed which is able to classify 63 forms of Urdu verbs successfully out of 66. Available Urdu language processing tools are very rare compared to other higher inflectional languages such as German, Turkish, etc., which have competitive morphological classifiers. However, the studies related to Urdu verb morphological classification are identified and a comparative study is presented in this article. In short, this work is a positive contribution to the community, and it provides sufficient information with promising results specifically on inflectional and derivational forms of Urdu verbs.
{"title":"Classifying Urdu Verbs Using Rule Based Approach","authors":"Muhammad Waseem","doi":"10.54692/lgurjcsit.2021.0501178","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2021.0501178","url":null,"abstract":"To make dictionaries complete and to keep their size restricted, there is an approach in the linguistic world to equip these dictionaries with morphological information. This module of morphological information is usually known as a morphological analyzer or morphological classifier, which normally contains the complete possible linguistic information about each word for that particular language and it also describes the rules of derivations from the root of a word and its various inflections, respectively. In this work, a classifier for Urdu verbs (CUV) is proposed which is still a challenging research issue, as Urdu is a language of high inflection and derivation. The available stemmers for Urdu do not provide enough information about inflectional and derivational forms of words. Also, morphological classifiers available for Urdu are not worthy of handling various problems and delivering results that prune errors. In our work, a rule based CUV is designed which is able to classify 63 forms of Urdu verbs successfully out of 66. Available Urdu language processing tools are very rare compared to other higher inflectional languages such as German, Turkish, etc., which have competitive morphological classifiers. However, the studies related to Urdu verb morphological classification are identified and a comparative study is presented in this article. In short, this work is a positive contribution to the community, and it provides sufficient information with promising results specifically on inflectional and derivational forms of Urdu verbs.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130277244","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：