International Journal of Computer and Communication Technology最新文献

{"title":"Data Analysis of Traditional Chinese Medicine Disease Diagnosis from the Perspective of Computational Sociology.","authors":"Haodong Zhou","doi":"10.47893/ijcct.2023.1451","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1451","url":null,"abstract":"In Traditional Chinese Medicine (TCM), the diagnosis and treatment of diseases typically involve viewing the patient as a system and considering both the intrinsic natural mechanisms of the disease and the external sociological factors. However, a comprehensive and scientific standard for understanding the external sociological factors in TCM diagnosis and treatment has not yet been established. The main reason for this is the complexity of computing these sociological factors due to their openness, multidimensionality, and heterogeneity. Drawing insights from computational sociology, this study explores the latent sociological factors in TCM disease diagnosis and treatment. It aims to obtain sociological factor data related to diseases from various online sources, such as internet-based medical consultation platforms and social networks. Through data analysis, it seeks to reveal the correlations between diseases and sociological latent factors. The ultimate goal is to establish a pre-diagnosis sociological factor database for TCM diseases. This endeavor serves as a foundation for developing a more scientific online TCM disease consultation system, providing references for TCM disease diagnosis and treatment, and offering evidence-based health behavioral recommendations for disease prevention.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130502721","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":"Analysis of Mental Health Problems Among Higher Education Students using Machine Learning","authors":"Ankita Satapathy, Saumendra Pattnaik, Sangappa Ramachandra Biradar, Saurav Kumar","doi":"10.47893/ijcct.2023.1449","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1449","url":null,"abstract":"Currently, mental health concerns pose a significant issue in Odisha. Generally, mental health problems affect a person's thoughts, feelings, actions, and communication. As per the 2017 National Health and Morbidity Survey (NHMS), one in five individuals in Odisha suffer from depression, two have anxiety, and one out of ten experiences stress. Additionally, students in higher education are at an elevated risk of developing mental health problems. However, helping a person with mental health concerns can be challenging due to difficulties in identifying the root causes of their condition. The main objectives of this study are to: 1. Explore mental health issues among higher education students. 2. Investigate the factors that contribute to these issues. 3. Assess the effectiveness of machine learning techniques in analyzing and predicting mental health problems among higher education students. Using computational modeling, this paper's findings will contribute to the ongoing discussion on mental health concerns in future research.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122488145","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":"DATA_SPHERE","authors":"V. R","doi":"10.47893/ijcct.2023.1434","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1434","url":null,"abstract":"This paper presents a comprehensive overview of Database Management Systems (DBMS) and their significance in modern information management. DBMS technology plays a crucial role in the storage, organisation, retrieval, and manipulation of vast amounts of data in various domains, ranging from business operations to scientific research. This abstract highlights the key aspects covered in the paper, including the evolution of DBMS, its architectural components, and the challenges and advancements in the field.\u0000\u0000The paper begins by discussing the historical development of DBMS, tracing its origins from file-based systems to the emergence of relational databases and the subsequent rise of object-oriented and NoSQL databases. We explore the motivations behind these advancements and their impact on data management.\u0000\u0000Next, we delve into the fundamental architectural components of a DBMS. We examine the storage layer, which encompasses data structures and access methods, and discuss different indexing techniques for efficient data retrieval. The query processing and optimization module are explored, focusing on query execution plans and cost-based optimization strategies. Additionally, we analyse the transaction management component, highlighting concepts such as ACID properties, concurrency control, and recovery mechanisms.\u0000\u0000The abstract also highlights the challenges faced by modern DBMS. With the proliferation of big data and the advent of cloud computing, scalability, availability, and performance have become critical concerns. We examine techniques such as parallel and distributed databases, replication, and sharding to address these challenges. Furthermore, we discuss the integration of DBMS with emerging technologies like machine learning and blockchain to leverage their capabilities in data analytics and secure data transactions.\u0000\u0000Lastly, the abstract touches upon recent advancements in DBMS, including the rise of graph databases for managing interconnected data, the adoption of in-memory databases for high-performance applications, and the exploration of new database models to handle unstructured and semi-structured data.\u0000\u0000In conclusion, this paper provides a comprehensive overview of DBMS, covering its historical evolution, architectural components, challenges, and recent advancements. By understanding the principles and advancements in DBMS, researchers and practitioners can effectively harness the power of data management systems to tackle the complexities of modern data-driven applications.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126537870","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":"INSIGHTQUEST FROM DATA","authors":"S. P.","doi":"10.47893/ijcct.2023.1437","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1437","url":null,"abstract":"Data mining is the process of discovering useful patterns and insights from large datasets, using statistical and machine learning techniques. It involves extracting knowledge from data and transforming it into an understandable structure for further use. Data mining algorithms can be used to analyze various types of data such as text, images, and videos, and can be applied to various domains such as finance, healthcare, and marketing. Data mining has many practical applications, such as customer segmentation, fraud detection, predictive modeling, and recommendation systems. It has become an important tool for businesses and organizations to gain insights from their data and make data-driven decisions. However, it also raises concerns about privacy, data protection, and ethics, as it involves handling large amounts of sensitive data. Therefore, ethical and responsible use of data mining techniques is crucial to ensure the protection of individual rights and the preservation of social values.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609777","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":"Graphical Image Rendering: Modeling, Animation of Facial or Wild Images","authors":"Rohit Kaushik, Chirag Vashisht, Eva Kaushik","doi":"10.47893/ijcct.2023.1446","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1446","url":null,"abstract":"In this comparative study, we intend to analyse different methodologies to perform 3-Dimensional modeling and printing, by using raw images as input without any supervision by a human. Since the input consists of only raw images, the foundation of the methods is finding symmetry in images. But the images that seem symmetric are not symmetric due to the perspective effect and utterance of other factors. The method uses factors like depth, albedo, point of view, and lighting from the input image to formulate 3D shapes. A 3D template model with feature points is created, and by deforming the 3D template model, a 3D model of the subject is then reconstructed from orthogonal photos. The number and locations of the proper amount of feature points are derived. Procrustes Analysis and Radial Basis Functions (RBFs) are used for the deformation. Images are then mapped onto the mesh following the deformations for realistic visualization. Characterization of the input image shows an asymmetric cause of shading, lighting, and albedo rendering the symmetry of images. The experiments show that using these methods can give exact 3D shapes of objects like human faces, cars, and cats.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458621","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":"Predicting Accurate Heart Attacks Using Logistic Regression","authors":"Vishal Baral, Pranati Palai, S. Nayak","doi":"10.47893/ijcct.2023.1444","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1444","url":null,"abstract":"A heart attack is one of the leading causes of death today. According to a large data population used as a training set for the algorithm for machine learning, classification is a technique for predicting the target class from input data. A difficulty in clinical data analytics is predicting heart attacks with greater precision. The focal point of this work is to analyze the heart attack dataset (Kaggle repository) to find a Machine learning classifier technique that predicts if a person is prone to a heart attack with maximum accuracy based on various health factors. The efficacy of the three classifiers, namely Logistic Regression, Random Forest, and Decision Tree, is demonstrated for predicting heart attack. This work compares the three classification algorithms among various factors. Logistic Regression outperforms all for predicting the values from the dataset accurately.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127318833","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":"Comparative Analysis of Data Mining Techniques for Heart Disease Prediction: A Focus on Neural Networks and Decision Trees","authors":"S. Panigrahi, Abantika Roy, Gargi Balabantaray, Karishma Rana","doi":"10.47893/ijcct.2023.1442","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1442","url":null,"abstract":"Heart disease is a general term used to describe numerous medical conditions that directly affect the heart and its various components. It is a prevalent health concern in modern times. The focus of this paper is to evaluate different data mining techniques for the prediction of heart disease, which have been introduced in recent years. The findings indicate that neural networks using 15 attributes demonstrate the best performance among all other data mining techniques. Additionally, the analysis concludes that decision trees, with the assistance of genetic algorithms and feature subset selection, also exhibit high accuracy. The study concludes that data mining techniques can effectively predict heart disease and that the choice of technique depends on the specific context of the analysis. The study suggests that decision trees and artificial neural network models are suitable for heart disease prediction. The study also recommends further research to explore the use of other data mining techniques for heart disease prediction.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500419","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":"Challenges and Solution for Identification of Plant Disease Using Machine Learning & IoT","authors":"Debasish Swapnesh Kumar Nayak, Saumendra Pattnaik, B. K. Pattanayak, Sonali Samal, Suprava Ranjan Laha","doi":"10.47893/ijcct.2023.1441","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1441","url":null,"abstract":"Internet of Thing (IoT) is a groundbreaking technology that has been introduced in the field of agriculture to improve the quality and quantity of food production. As agriculture plays a vital role in feeding most of the world's population, the increasing demand for food has led to a rise in food grain production. The identification of plant diseases is a critical task for farmers and agronomists as it enables them to take proactive measures to prevent the spread of diseases, protect crops, and maximize yields. Traditional methods of plant disease detection involve visual inspections by experts, which can be time-consuming and often subject to human error. However, with technological advancements, IoT and Machine Learning (ML) has emerged as promising solution for automating and improving plant disease identification. This paper explores the challenges and solutions for identifying plant diseases using IoT and ML. The challenges discussed include data collection, quality, scalability, and interpretability. The proposed solutions include using sensor networks, data pre-processing techniques, transfer learning, and explainable AI.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129643871","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":"A CREATIVE JOURNEY INTO BIG DATA ANALYSTS","authors":"Ravi M","doi":"10.47893/ijcct.2023.1435","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1435","url":null,"abstract":"A big data analyst is a professional who specializes in analyzing and interpreting large and complex sets of data. They use various tools and techniques to extract insights and trends from the data, which can help businesses make informed decisions and improve their operations.To become a big data analyst, you typically need a strong background in statistics, computer science, and data analysis. You may also need experience working with big data tools such as Hadoop, Spark, and NoSQL databases. Additionally, strong communication and collaboration skills are important, as big data analysts often work with teams of other data professionals and stakeholders within the organization.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"34 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124255265","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":"Forecasting Stock Market Indices Using Gated Recurrent Unit (GRU) Based Ensemble Models: LSTM-GRU","authors":"Nrusingha Tripathy, Surabi Parida, S. Nayak","doi":"10.47893/ijcct.2023.1443","DOIUrl":"https://doi.org/10.47893/ijcct.2023.1443","url":null,"abstract":"A \"time sequence analysis\" is a particular method for looking at a group of data points gathered over a long period of time. Instead of merely randomly or infrequently, time series analyzers gather information from data points over a predetermined length of time at scheduled times. But this kind of research requires more than just accumulating data over time. Data in time series may be analyzed to illustrate how variables change over time, which makes them different from other types of data. To put it another way, time is a crucial element since it demonstrates how the data changes over the period of the information and the outcomes. It offers a predetermined architecture of data dependencies as well as an extra data source. Time Series forecasting is a crucial field in deep learning because many forecasting issues have a temporal component. A time series is a collection of observations that are made sequentially across time. In this study, we examine distinct machine learning, deep learning and ensemble model algorithms to predict Nike stock price. We are going to use the Nike stock price data from January 2006 to January 2018 and make predictions accordingly. The outcome demonstrates that the hybrid LSTM-GRU model outperformed the other models in terms of performance.","PeriodicalId":220394,"journal":{"name":"International Journal of Computer and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129108160","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}