Lahore Garrison University research journal of computer science and information technology最新文献

NAVIGATING EMOTIONS ACROSS BORDERS: DEEP LEARNING-DRIVEN LOCATION-INFORMED SENTIMENT ANALYSIS OF TWITTER 跨越国界的情感导航:深度学习驱动的twitter位置信息情感分析

Lahore Garrison University research journal of computer science and information technology Pub Date : 2023-10-19 DOI: 10.54692/lgurjcsit.2023.073493

None Muhammad Waleed Karam, Azhar Imran, None Ali Haider Khan, None Sadaf Idrees

{"title":"NAVIGATING EMOTIONS ACROSS BORDERS: DEEP LEARNING-DRIVEN LOCATION-INFORMED SENTIMENT ANALYSIS OF TWITTER","authors":"None Muhammad Waleed Karam, Azhar Imran, None Ali Haider Khan, None Sadaf Idrees","doi":"10.54692/lgurjcsit.2023.073493","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.073493","url":null,"abstract":"Emotion assessment, a pivotal domain in the realm of natural language processing, holds significant importance in comprehending the feelings expressed through textual content. It has progressed to include the intricate interplay of emotions found in textual information. This paper introduces a pioneering approach to sentiment analysis by amalgamating deep learning techniques and geographic context within the realm of Twitter data. Leveraging an expanded sentiment class set that includes positive, negative, neutral, mixed, ambiguous, happy, sad, angry, fearful, and surprised. Our framework aptly captures diverse emotional expressions. Incorporating location-based sentiment analysis unveils cross-border sentiment dynamics, enriching our understanding of how emotions resonate within various geographical regions. We present a meticulously designed deep learning model that seamlessly integrates textual content and location information. Through the utilization of text vectorization, embedding layers, and advanced classification techniques, our model achieves exceptional accuracy, F1-score, precision, and recall values. The temporal analysis of tweet timestamps uncovers temporal engagement trends, while the examination of tweet lengths underscores the dynamic range of expression within the Twitter character limitation. Furthermore, our investigation into the locations reveals Twitter's global presence, with the United States, United Kingdom, and Ukraine emerging as key hubs of activity. This geographical insight augments our comprehension of the platform's diverse user interactions. This paper not only offers insights into sentiment analysis but also paves the way for future research exploring sentiment dynamics, language variations, and real-time interactions within the Twitter landscape.","PeriodicalId":480809,"journal":{"name":"Lahore Garrison University research journal of computer science and information technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781335","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

Secure Cloud Based IoT Data Storage 安全基于云的物联网数据存储

Lahore Garrison University research journal of computer science and information technology Pub Date : 2023-10-18 DOI: 10.54692/lgurjcsit.2023.073477

None Nazia Shoukat

引用次数: 0

AN IMPROVED SVM AND CONVOLUTIONAL NEURAL NETWORK-BASED GARBAGE CLASSIFICATION SYSTEM (GCLS) AUGMENTED WITH TRANSFER LEARNING AND OBJECT DETECTION API 一种基于支持向量机和卷积神经网络的垃圾分类系统(gcls)，增强了迁移学习和目标检测API

Lahore Garrison University research journal of computer science and information technology Pub Date : 2023-10-10 DOI: 10.54692/lgurjcsit.2023.073471

Bushra Jamil

引用次数: 0

AN AGRICULTURAL INTERNET OF THINGS (A-IOT) BASED INTELLIGENT SYSTEM FOR DISEASE PREDICTION USING TRANSFER LEARNING, A CASE STUDY 基于农业物联网(a - iot)的疾病预测智能系统，应用迁移学习，案例研究

Lahore Garrison University research journal of computer science and information technology Pub Date : 2023-10-10 DOI: 10.54692/lgurjcsit.2023.073492

Tahir Khan

{"title":"AN AGRICULTURAL INTERNET OF THINGS (A-IOT) BASED INTELLIGENT SYSTEM FOR DISEASE PREDICTION USING TRANSFER LEARNING, A CASE STUDY","authors":"Tahir Khan","doi":"10.54692/lgurjcsit.2023.073492","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.073492","url":null,"abstract":"The agricultural Internet of Things (IoT) has altered agricultural output in unprecedented ways. In addition to raising agricultural productivity, it may also significantly raise product quality, lower labor costs, boost farmer income, and achieve agricultural modernization and intelligence. To predict diseases in the agricultural sector, this article proposed an IoT-based smart system for agriculture. The current state of agricultural IoT is first illustrated, along with a summary of its system architecture. In order to predict diseases in the agricultural domain and advance agricultural IoT, an intelligent system based on agriculture is being proposed in this study. The MATLAB 2020a tool is used for simulation and results. In the proposed industrial IoT based intelligent system, a transfer learning model is applied for the training and validation of rice leaf disease prediction in agriculture industry 4.0. The result of the proposed industrial IoT based intelligent system achieved 96.95% accuracy, which is better than the state-of-the-art published methods.","PeriodicalId":480809,"journal":{"name":"Lahore Garrison University research journal of computer science and information technology","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136360265","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 Comparative Study of Parallel and Distributed Big Data programming models: Methodologies, Challenges and Future Directions 并行和分布式大数据编程模型的比较研究:方法、挑战和未来方向

Lahore Garrison University research journal of computer science and information technology Pub Date : 2023-10-09 DOI: 10.54692/lgurjcsit.2023.073365

None Fiza Gulzar Hussain, None Muhammad Wasim, None Ayesha Nasir

{"title":"A Comparative Study of Parallel and Distributed Big Data programming models: Methodologies, Challenges and Future Directions","authors":"None Fiza Gulzar Hussain, None Muhammad Wasim, None Ayesha Nasir","doi":"10.54692/lgurjcsit.2023.073365","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2023.073365","url":null,"abstract":"According to a survey conducted in 2021, users share about 4 petabytes of data on Facebook daily. The exponential increase in data (called big data) plays a vital role in machine learning, internet of things (IoT), and business intelligence applications. Due to the rapid increase in big data, research in big data programming models gained much interest in the past decade. Today, many programming paradigms exist to handle big data, and selecting an appropriate model for a project is critical for its success. This study provides an in-depth analysis of big data programming models such as MapReduce, Directed Acyclic Graph (DAG), Bulk Synchronous Parallel (BSP), and SQL. We conduct a comparative study of distributed and parallel big data programming models and categorize these models into three classes: traditional data processing, graph-based processing, and query-based processing models. Furthermore, we evaluate these programming models based on different parameters like performance, data processing, storage, fault-tolerant, suitable language, and machine learning support. Finally, we highlight the benchmark datasets used for big data programming models and discuss the challenges of models along with future directions for the research community.","PeriodicalId":480809,"journal":{"name":"Lahore Garrison University research journal of computer science and information technology","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135148964","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

An ONTOLOGY BASED APPROACH FOR TRAFFIC DENSITY ESTIMATION IN INTELLIGENT TRANSPORTATION SYSTEMS 基于本体的智能交通系统交通密度估计方法

Lahore Garrison University research journal of computer science and information technology Pub Date : 2023-10-09 DOI: 10.54692/lgurjcsit.2023.073461

None Hira

引用次数: 0