Lahore Garrison University Research Journal of Computer Science and Information Technology最新文献_第6页

A Survey on Emotion Detection from Text in Social Media Platforms 社交媒体平台文本情感检测研究

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-06-21 DOI: 10.54692/lgurjcsit.2021.0502208

M. Usman Ashraf

{"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":"11 1","pages":"0"},"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}

引用次数: 2

A Novel Model for Explainable Hostel Recommender System Using Hybrid Filtering 基于混合过滤的可解释旅馆推荐系统模型

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-06-21 DOI: 10.54692/lgurjcsit.2021.0502203

Shahzad Ahmed Khan

引用次数: 1

Using Data Mining Technique to Measure the Impact of COVID-19: 1st Wave on the Stock Market of Top Fifteen Affected Countries 利用数据挖掘技术衡量COVID-19:第一波对前15个受影响国家股市的影响

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-06-21 DOI: 10.54692/lgurjcsit.2021.0502204

Zubair Akbar

引用次数: 1

Time Dependent Popularity Caching Scheme for NDN Based MANETs 基于NDN的manet的时变流行度缓存方案

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501174

Ehsan Elahi

引用次数: 0

Analysis of Advantages and Problems in Teaching and Assessment with Online System during Covid-19 新冠肺炎疫情期间在线教学评估的优势与问题分析

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501172

Aftab Ahmad Malik

{"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":"71 1","pages":"0"},"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}

引用次数: 1

Traffic Intensity Based Energy Efficiency Architecture for Data-Centers 基于流量强度的数据中心能效体系结构

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501177

Salman Qadri

{"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":"8 1","pages":"0"},"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}

引用次数: 0

A Spatial Model of K-Nearest Neighbors for Classification of Cotton (Gossypium) Varieties based on Image Segmentation 基于图像分割的棉花品种分类空间k近邻模型

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501173

Salman Qadri

{"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":"39 1","pages":"0"},"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}

引用次数: 5

Information Security for Cloud using Image Steganography 基于图像隐写术的云信息安全

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501171

Muhammad Amjad Khan

引用次数: 0

Trading Algorithm Model Based on Technical Indicators 基于技术指标的交易算法模型

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501176

Muhammad Khawar Bashir

引用次数: 0

Classifying Urdu Verbs Using Rule Based Approach 基于规则的乌尔都语动词分类方法

Lahore Garrison University Research Journal of Computer Science and Information Technology Pub Date : 2021-02-09 DOI: 10.54692/lgurjcsit.2021.0501178

Muhammad Waseem

{"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":"1 1","pages":"0"},"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}

引用次数: 0