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International Journal of Computer Science and Technology
IJCST Vol 15 Issue 1 (Jan-March 2024)

S.No. Research Topic Paper ID Download
01

Predicting Heart Disease Using Standalone Application

Pinjarla Poornamohan, Suresh Kumar Meenige

Abstract

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

Full Paper

IJCST/143/1/A-1041
02

Symptom-Based Multi-Disease Prediction: Machine Learning Approach for Comprehensive Healthcare Diagnosis

Murali Kalipindi, Sunkarapalli Srinivas

Abstract

Due to machine learning progress in biomedical and healthcare communities, accurate study of medical data benefits early diseaserecognition, patient care and community services. When the quality of medical data is incomplete the exactness of study is reduced. Moreover, different regions exhibit unique appearances of certain regional diseases, which may result in weakening the prediction of disease outbreaks. In the proposed system, it provides machine learning algorithms for effective prediction of various disease occurrences in disease-frequent societies. It experiments the altered estimate models over real-life hospital data collected. To overcome the difficulty of incomplete data, it uses a latent factor model to rebuild the missing data. It experiments on a regional chronic illness ofcerebral infarction. Using structured and unstructured data from hospital it uses Machine Learning algorithm. It predicts probable diseases by mining data sets such as Covid-19, chronic kidney disease and heart disease. To the best of our knowledge in the areaof medical big data analytics none of the existing work focused on both data types. Compared to several typical estimate algorithms, the calculation exactness of our proposed algorithm reaches 94.8% with a convergence speed which isfaster than that of the machine learning disease risk prediction algorithm.

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IJCST/143/1/A-1042
03

Cryptocurrency Price Analysis With Artificial Intelligence

Guttula Eswararao, Kondapalli Paparao

Abstract

Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try to understand the factors that influence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN. However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This project provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model.

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IJCST/143/1/A-1043
04

Detection of Fake News Artificial Intelligence Using NLP

Perabathula Latha, Kondapalli Paparao

Abstract

In this modern era, everyone relies on various online resources for news. Since there are many social media platforms like Facebook, Twitter etc., news spread rapidly among millions of users. However, there may be some misleading content for damaging the reputation of people or firms. The fake news propagators intentionally spread fake news to affect public opinion on certain matters. So, to stop spreading this fake news and to rescue innocent people from fake news propagators, detection of fake news at an early stage is very essential. There are various techniques exists to detect fake news, among them natural language processing is one of the techniques which works effectively and efficiently. In natural language processing, text pre-processing techniques such as regular expression, tokenization and lemmatization is used before vectorization. Vectorization is vectorizing the data into N-gram vectors or sequence vectors using terms frequency-inverse document frequency (TF-IDF) or one-hot encoding respectively. N-grams concept is mainly used to enhance the proposed model. In order to observe the accuracy of the model, classification algorithms of machine learning can be used. Fake news detection aims to provide the user with the ability to classify the news as fake or real.

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IJCST/143/1/A-1044
05

Reducing Load At Data Center Using Load Balancing Algorithms

Maney Saketh Naidu, P Ramakrishna

Abstract

Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature’s findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Make.

Full Paper

IJCST/143/1/A-1045
06

Calculation of Calories in Food and Body Mass Index Prediction Using Deep Learning

Pitta Vipin Raj, V Veerendra Subhash

Abstract

Obesity is a major public health issue worldwide, and it is closely linked to the consumption of excess calories. Therefore, accurately estimating food calorie intake and predicting body mass index (BMI) is crucial in managing and preventing obesity. Traditional methods for estimating food calorie intake, such as self-reported food diaries, are often unreliable due to human error and biases. In recent years, advances in computer vision and machine learning have enabled the development of automated food calorie estimation systems using food images. However, these methods still have room for improvement in terms of accuracy and practicality. This project aims to address these challenges by developing a robust and accurate system for food calorie estimation and BMI prediction using deep learning and machine learning techniques. The proposed system can have practical applications in nutrition, healthcare, and fitness, enabling individuals to track their calorie intake and manage their weight more effectively. This project aims to develop a model for food calorie estimation and BMI prediction using deep learning and machine learning techniques. The proposed system will use a dataset of food images and their corresponding calorie information to train a convolutional neural network (CNN) to recognize the food items and estimate their calorie content. The system will also use additional demographic information, such as age, gender, height, and weight, to predict the user’s BMI using a regression model. The accuracy of the models will be evaluated using metrics such as mean absolute error and root mean square error. The results will demonstrate the feasibility of using deep learning and machine learning techniques for accurate food calorie estimation and BMI prediction, which can have practical applications in nutrition and healthcare.

Full Paper

IJCST/143/1/A-1046