keyboard_arrow_up
Top 10 read research articles in the field of Computer Science & Information Technology @ 2024

Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value

    Godwin Ogbuabor1 and Ugwoke, F. N2, 1University of Lincoln, United Kingdom and 2Michael Okpara University of Agriculture Umudike, Nigeria

    ABSTRACT

    The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining interesting research areas. Data mining tools and techniques help to discover and understand hidden patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate clustering method and optimal number of clusters in healthcare data can be confusing and difficult most times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but it is very difficult for people with little knowledge of data mining to choose suitable clustering algorithms. This paper aims to analyze clustering techniques using healthcare dataset, in order to determine suitable algorithms which can bring the optimized group clusters. Performances of two clustering algorithms (Kmeans and DBSCAN) were compared using Silhouette score values. Firstly, we analyzed K-means algorithm using different number of clusters (K) and different distance metrics. Secondly, we analyzed DBSCAN algorithm using different minimum number of points required to form a cluster (minPts) and different distance metrics. The experimental result indicates that both K-means and DBSCAN algorithms have strong intra-cluster cohesion and inter-cluster separation. Based on the analysis, K-means algorithm performed better compare to DBSCAN algorithm in terms of clustering accuracy and execution time.

    KEYWORDS

    Dataset, Clustering, Healthcare data, Silhouette score value, K-means, DBSCAN


    For More Details :
    https://aircconline.com/ijcsit/V10N2/10218ijcsit03.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2018_curr.html




Security Threats on Cloud Computing Vulnerabilities

    Te-Shun Chou, East Carolina University, U.S.A

    ABSTRACT

    Clouds provide a powerful computing platform that enables individuals and organizations to perform variety levels of tasks such as: use of online storage space, adoption of business applications, development of customized computer software, and creation of a “realistic” network environment. In previous years, the number of people using cloud services has dramatically increased and lots of data has been stored in cloud computing environments. In the meantime, data breaches to cloud services are also increasing every year due to hackers who are always trying to exploit the security vulnerabilities of the architecture of cloud. In this paper, three cloud service models were compared; cloud security risks and threats were investigated based on the nature of the cloud service models. Real world cloud attacks were included to demonstrate the techniques that hackers used against cloud computing systems. In addition, counter measures to cloud security breaches are presented.

    KEYWORDS

    Cloud computing, cloud security threats and countermeasures, cloud service models


    For More Details :
    http://airccse.org/journal/jcsit/5313ijcsit06.pdf


    Volume Link :
    http://airccse.org/journal/ijcsit2013_curr.html




Data Mining Model Performance of Sales Predictive Algorithms Based on Rapidminer Workflows

    Alessandro Massaro, Vincenzo Maritati and Angelo Galiano, Dyrecta Lab, Italy

    ABSTRACT

    By applying RapidMiner workflows has been processed a dataset originated from different data files, and containing information about the sales over three years of a large chain of retail stores. Subsequently, has been constructed a Deep Learning model performing a predictive algorithm suitable for sales forecasting. This model is based on artificial neural network –ANN- algorithm able to learn the model starting from sales historical data and by pre-processing the data. The best built model uses a multilayer neural network together with an “optimized operator” able to find automatically the best parameter setting of the implemented algorithm. In order to prove the best performing predictive model, other machine learning algorithms have been tested. The performance comparison has been performed between Support Vector Machine –SVM-, k-Nearest Neighbor k-NN-,Gradient Boosted Trees, Decision Trees, and Deep Learning algorithms. The comparison of the degree of correlation between real and predicted values, the average absolute error and the relative average error proved that ANN exhibited the best performance. The Gradient Boosted Trees approach represents an alternative approach having the second best performance. The case of study has been developed within the framework of an industry project oriented on the integration of high performance data mining models able to predict sales using–ERP- and customer relationship management –CRM- tools.

    KEYWORDS

    RapidMiner, Neural Network, Deep Learning, Gradient Boosted Trees, Data Mining Performance, Sales Prediction.


    For More Details :
    http://aircconline.com/ijcsit/V10N3/10318ijcsit03.pdf


    Volume Link :
    http://airccse.org/journal/ijcsit2018_curr.html




Product Sentiment Analysis for Amazon Reviews

    Arwa S. M. AlQahtani, Princess Nourah bint Abdulrahman University, Saudi Arabia

    ABSTRACT

    Recently, Ecommerce has Witnessed Rapid Development. As A Result, Online Purchasing has grown, and that has led to Growth in Online Customer Reviews of Products. The Implied Opinions in Customer Reviews Have a Massive Influence on Customer's Decision Purchasing, Since the Customer's Opinion About the Product is Influenced by Other Consumers' Recommendations or Complaints. This Research Provides an Analysis of the Amazon Reviews Dataset and Studies Sentiment Classification with Different Machine Learning Approaches. First, the Reviews were Transformed into Vector Representation using different Techniques, I.E., Bag-Of-Words, Tf-Idf, and Glove. Then, we Trained Various Machine Learning Algorithms, I.E., Logistic Regression, Random Forest, Naïve Bayes, Bidirectional Long-Short Term Memory, and Bert. After That, We Evaluated the Models using Accuracy, F1-Score, Precision, Recall, and Cross-Entropy Loss Function. Then, We Analyized The Best Performance Model in Order to Investigate Its Sentiment Classification. The Experiment was Conducted on Multiclass Classifications, Then we Selected the Best Performing Model And Re-Trained It on the Binary

    KEYWORDS

    RapidMiner, Neural Network, Deep Learning, Gradient Boosted Trees, Data Mining Performance, Sales Prediction.


    For More Details :
    https://aircconline.com/ijcsit/V13N3/13321ijcsit02.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2021_curr.html




Future and Challenges of Internet of Things

    Falguni Jindal, Rishabh Jamar and Prathamesh Churi, SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering, India

    ABSTRACT

    The world is moving forward at a fast pace, and the credit goes to ever growing technology. One such concept is IOT (Internet of things) with which automation is no longer a virtual reality. IOT connects various non-living objects through the internet and enables them to share information with their community network to automate processes for humans and makes their lives easier. The paper presents the future challenges of IoT , such as the technical (connectivity , compatibility and longevity , standards , intelligent analysis and actions , security), business ( investment , modest revenue model etc. ), societal (changing demands , new devices, expense, customer confidence etc. ) and legal challenges ( laws, regulations, procedures, policies etc. ). A section also discusses the various myths that might hamper the progress of IOT, security of data being the most critical factor of all. An optimistic approach to people in adopting the unfolding changes brought by IOT will also help in its growth

    KEYWORDS

    IoT, Internet of Things, Security, Sensors


    For More Details :
    https://aircconline.com/ijcsit/V10N2/10218ijcsit02.pdf


    Volume Link :
    http://airccse.org/journal/ijcsit2018_curr.html




Monitoring Student Attendance Using A Smart System at Taif University

    Saleh Alghamdi, Taif University, Saudi Arabia

    ABSTRACT

    The university system in the Kingdom of Saudi Arabia is concerned with student attendance for lectures, and it is the responsibility of lecturers to monitor student attendance for each lecture. By the end of the semester, students get an attendance register indicating which lectures the student has attended and it reports the calculated percentage for each student’s attendance in each course. Universities have regulated the mechanisms and the acceptable percentages of student absence. The process for a lecturer to manually check student attendance consumes a lot of time and effort, either during the lecture or when in the process of emptying absenteeism and inserting it into the university’s electronic system. Therefore, Saudi universities compete to find modern methods of checking student attendance that will avoid the disadvantages of manually taking attendance. For this reason, they have produced electronic attendance systems, for example, using a student's fingerprint, an eye recognition system, or a mobile phone system to read a QR code designed for the same purpose. All of these systems have the disadvantage that they consume a lot of time, as all students have to line up at the fingerprint reader or the eye detector for identification. Therefore, the problem of the consumption of lecture time is still present, even with these modern systems. Therefore, the aim of this research is to propose a smart mobile application that is able to check the attendance of students without having to consume lecture time or require any effort from the lecturer. The system automatically recognizes the attendance of students through their university ID cards. Each lecturer would use his/ her own mobile phone to use the proposed system to check the attendance of students instead of using manual method to register the attendance of students and the students’ ID cards that are detected by coming within range of the lecturer reader would represent present students, and missing student ID cards represent absent students

    KEYWORDS

    Context Awareness, RFID, Monitoring Student Attendance.


    For More Details :
    https://aircconline.com/ijcsit/V11N1/11119ijcsit08.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2019_curr.html




An Algorithm for Predictive Data Mining Approach in Medical Diagnosis

    Shakuntala Jatav and Vivek Sharmam, TIT College - Bhopal, India

    ABSTRACT

    The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.

    KEYWORDS

    Data Mining, Clinical Decision Support System, Disease Prediction, Classification, SVM, RF.


    For More Details :
    https://aircconline.com/ijcsit/V10N2/10218ijcsit03.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2018_curr.html




An Exploration of the Factors Affecting User's Satisfaction with Mobile Payments

    Lisa Y. Chen and Wan-Ning Wu, I-Shou University, Taiwan

    ABSTRACT

    Mobile payment allows consumers to make more flexible payments through convenient mobile devices. While mobile payment is easy and time save, the operation and security of mobile payment must ensure that the payment is fast, convenient, reliable and safety in order to increase the users’ satisfaction. Therefore, this study based on technology acceptance model to explore the impact of external variables through perceived usefulness and perceived ease of use on users’ satisfaction. The data analysis methods used in this study are descriptive statistical analysis, reliability and validity analysis, Pearson correlation analysis and regression analysis to verify the hypotheses. The results show that all hypotheses are supported. However, mobile payment is still subject to many restrictions on development and there are limited related researches. The results of this study provided insight into the factors that affect the users’ satisfaction for mobile payment. Related services development of mobile payment and future research suggestions are also offered.

    KEYWORDS

    Mobile Payment, Technology Acceptance Model, Users’ satisfaction.


    For More Details :
    https://aircconline.com/ijcsit/V9N3/9317ijcsit08.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2017_curr.html




Environmental Monitoring and Controlling System for Mushroom Farm with Online Interface

    Arjuna Marzuki and Soh Yan Ying, Universiti Sains Malaysia, Malaysia

    ABSTRACT

    sectors minimizes the environmental impact on crops, helps in increasing yield and saving cost of operation. Among all agriculture industries in Malaysia, the mushroom industry is a comparatively new and small. As most of the mushroom farms in Malaysia are small-scaled, their production capability is limited by inadequate environmental control system and the lack of financial resources to upgrade the systems. This paper presents an environmental monitoring and controlling system to monitor and control the environmental conditions in a mushroom farm. It enables user to monitor temperature, humidity, carbon dioxide concentration and light intensity in a mushroom farm on an android device by using ThingSpeak online platform. The control algorithm is able to control devices in a mushroom farm automatically based on feedback from the sensors to maintain the environment in an optimum condition for mushroom growth. The measured percentage error of temperature, humidity, carbon dioxide and the light using the developed system was as low as 0.4%, 1.5%, 2.2% and 1.34% respectively

    KEYWORDS

    Mobile Payment, Technology Acceptance Model, Users’ satisfaction.


    For More Details :
    https://aircconline.com/ijcsit/V9N4/9417ijcsit02.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2017_curr.html




Edge Detection Techniques For Image Segmentation

    Muthukrishnan.R and M.Radha, Bharathiar University, India

    ABSTRACT

    Interpretation of image contents is one of the objectives in computer vision specifically in image processing. In this era it has received much awareness of researchers. In image interpretation the partition of the image into object and background is a severe step. Segmentation separates an image into its component regions or objects. Image segmentation t needs to segment the object from the background to read the image properly and identify the content of the image carefully. In this context, edge detection is a fundamental tool for image segmentation. In this paper an attempt is made to study the performance of most commonly used edge detection techniques for image segmentation and also the comparison of these techniques is carried out with an experiment by using MATLAB software.

    KEYWORDS

    Computer Vision , Image Segmentation , Edge detection, MATLAB.


    For More Details :
    https://airccse.org/journal/jcsit/1211csit20.pdf


    Volume Link :
    https://airccse.org/journal/ijcsit2011_curr.html








menu
Reach Us

emailsecretary@cseij.org


emailcseijsecretary@yahoo.com

close