Volume-9 Issue-7S, May 2020 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

G10170597S20

IJITEE Journal — Fri, 18 Aug 2023 10:18:49 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. 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Heart Disease Prediction Methods
Mansi Gunsai¹, Shreya Patel², Kinjal V. Joshi³
¹Mansi Gunsai, Department of Computer Engineering, G H Patel College of Engineering & Technology, Anand, (Gujarat), India.

²ShreyaPatel, Department of Computer Engineering, G H Patel College of Engineering & Technology, Anand, (Gujarat), India.

³Prof Kinjal Joshi, Department of Computer Engineering, G H Patel College of Engineering & Technology, Anand, (Gujarat), India.

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In recent times, heart diseases are considered one of the deadliest causes of mortality and morbidity among the population of the world. Predicting the probability of the occurrence of cardiovascular diseases has become one of the most important objectives of the medical analysis system. The conventional methods have proved to be inefficient in prior prediction of heart diseases because of several contributing risk factors like diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and others. Due to such limitations, medical practitioners rely on various modern Machine Learning and Data Mining approaches such as ANN, Naïve Bayes, SVM, etc. Such models have proved to be effective in providing accurate predictions from the huge amount of medical data available. The main aim of this paper is to analyse various machine learning approaches adopted in different research works and to deduce which techniques are most beneficial and precise.

Keywords: Heart Disease Prediction, Machine Learning, Artificial Neural Network, Naïve Bayes, Decision Tree, Genetic Algorithm.
Scope of the Article: Regression and Prediction

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G10110597S20

IJITEE Journal — Fri, 18 Aug 2023 10:11:17 +0000

Different Ray-Casting Algorithm Implementations for Volume Rendering
Palak Chatwani¹, Shivani Shah², Purvi Ramanuj³
¹Ms. Palak Chatwani, Pursuing M.E., L.D. College of Engineering and Doing Her Dissertation DWD/MRG/EPSA SAC, ISRO.

²Mrs. Shivani Shah, Scientist-SF, DWD/MRG/EPSA SAC, ISRO. She Works As A MOSDAC Administrator As Well As Principal Investigator Cloud Retrieval From INSAT Series of Satellite.

Abstract: Volume Rendering is the way to achieve 3D visualization. Volume Rendering is used for visualization of 2D projections of 3D data. In volume rendering techniques, direct volume rendering techniques (DVR) can be divided into image order and object order. Image order technique can be achieved by ray-casting algorithm. Ray-casting algorithm is used for raysurface interaction tests to solve problems in computer graphics like collision detection and hidden surface removal. In DVR, the ray is pushed through the object and 3D scalar field of interest is sampled along the ray inside the object. Over the years, different approaches towards this algorithm took place. This paper represents the review and analysis of different approaches of ray-casting algorithm.

Keywords: Volume Rendering, Ray-Casting, 3D Visualization.
Scope of the Article: Algorithm Engineering

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G10230597S20

IJITEE Journal — Fri, 18 Aug 2023 10:00:45 +0000

An Introduction on Interpretable Machine Learning
Neel Pradip Shah¹, Sheetal Jeshwani², Pavni Bhatt³
¹Neel Pradip Shah, Masters’ Student, WIAI Faculty, University of Bamberg, Germany.

²Sheetal Jeshwani, Masters’ Student, WIAI Faculty, University of Bamberg, Germany.

³Pavni Bhatt, Data Analyst, MTLB India Private Ltd., India.

Abstract: As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical practices and fair uses arise, which has motivated the research community to look inside and develop methods to interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help with the ethical questions but also can provide various insights into the working of these machine learning models, which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in many machine learning applications, the feature of interpretability is the primary value that they offer. However, in practice, many developers select models based on the accuracy score and disregarding the level of interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable Machine learning, and the methods used for interpretation and explanations.

Keywords: Machine Learning, Interpretability, Black Box Models, Explainable Artificial Intelligence.
Scope of the Article: Machine Learning

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G10200597S20

IJITEE Journal — Fri, 18 Aug 2023 09:50:41 +0000

A Comparative Study of Classification Techniques for P300 Speller
VaishaliPatelia¹, Maulika S. Patel²
¹VaishaliPatelia, Department of Computer Engineering, G. H. Patel College of Engineering & Technology, Gujarat Technological University, V.V. Nagar, (Gujarat), India.

²Maulika S. Patel, Professor & Head, Department of Computer Engineering, G. H. Patel College of Engineering and Technology, Gujarat Technological University, V.V. Nagar, (Gujarat), India.

Abstract: P300 speller in Brain Computer Interface (BCI) allows locked-in or completely paralyzed patients to communicate with humans. To achieve the performance of characterization and increase accuracy, machine learning techniques are used. The study is about an event related potential (ERP) P300 signal detection and classification using various machine learning algorithms. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) are used to classify P300 and Non-P300 signal from Electroencephalography (EEG) signal. The performance of the system is evaluated based on f1-score using BCI competition III dataset II. In our system, we used LDA and SVM classification algorithms. Both the classifiers gave 91.0% classification accuracy.

Keywords: Brain Computer Interface, P300 Speller, Event Related Potential, Linear Discriminant Analysis, Support Vector Machine.
Scope of the Article: Classification

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G10190597S20

IJITEE Journal — Fri, 18 Aug 2023 09:43:07 +0000

Sentiment Analysis for Twitter Data
Deep Kaneria¹, Brijesh Patel²
¹Deep Kaneria, Department of Computer Engineering, G H Patel College of Engineering & Technology, Vallabh Vidyanagar, Anand (Gujarat), India.

²Brijesh Patel, Assistant Professor, Department of Computer Engineering, GCET, Greater Noida (Uttar Pradesh), India.

Abstract: With the advancements in web technology and its growth, there’s an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that’s helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes & Support Vector Machine, we provide research on twitter data.

Keywords: Machine Learning (ML), Naïve Bayes (NB), Twitter, Support Vector Machine (SVM), Sentiment Analysis (SA).
Scope of the Article: Data Analytic

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G10130597S20

IJITEE Journal — Fri, 18 Aug 2023 09:35:45 +0000

Automatic Image Captioning Methods
Ruchitesh Malukani¹, Nihaal Subhash², Chhaya Zala³
¹Ruchitesh Malukani, Department of Computer Engineering, G. H. Patel College of Engineering & Technology, Anand, India.

²Nihaal Subhash, Department of Computer Engineering, G. H. Patel College of Engineering & Technology, Anand, India.

³Prof. Chhaya Zala, Department of Computer Engineering, G. H. Patel College of Engineering & Technology, Anand, India.

Abstract: A language known to humans is a natural language. In computer science it is the most challenging task to make the computers understand the natural languages and generating caption automatically from the given image. While a lot of work has been done, the total solution to this problem has been demonstrated daunting so far. Image captioning is a crucial job involving linguistic image understanding and the ability to generate interpretation of sentences with proper and accurate structure. It requires expertise in Image processing and natural language processing. The publishers suggest in this practice a system using the multilayer Convolutional Neural Network (CNN) to generate language describing the images and Long Short Term Memory (LSTM) to concisely frame relevant phrases using the driven keywords. We aim in this article to provide a brief overview of current methods and algorithms of image captioning using deep learning. We also address datasets and measurement criteria widely used for the same.

Keywords: Image Captioning, Deep Learning, Computer Vision, Natural Language Processing, CNN, RNN, LSTM.
Scope of the Article: Image Security

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G10100597S20

IJITEE Journal — Fri, 18 Aug 2023 09:26:33 +0000

Real-Time Lane Detection and Object Recognition in Self-Driving Car using YOLO neural network and Computer Vision
Nikita Mesvaniya¹, Meghna Dhruva², Ishita Khared³, Krishna Adhia⁴
¹Nikita Mesvaniya, Department of Computer Engineering, Darshan Institute of Engineering and Technology, Rajkot (Gujarat), India.

²Meghna Dhruva, Department of Computer Engineering, BVM Engineering College, VVNagar, (Gujarat), India.

³Ishita Khared, Department of Computer Engineering, Darshan Institute of Engineering and Technology, Rajkot (Gujarat), India.

⁴Krishna Adhia, Department of Computer Engineering, Darshan Institute of Engineering and Technology, Rajkot (Gujarat), India.

Abstract: The Darwinism of Artificial Intelligence and robotics has grown up incredibly. Recently, there are a lot of progress have been undertaken in the context of Autonomous vehicle. Robo-car or self driving car consist many module like localization and mapping, scene understanding, movement planning, and driver state. In movement planning lane perception and recognition of the object plays vital role. This proposed state-of-art recognizes the road track in the video‘s frame and perform lane detection using canny edge detector and Hough transform algorithm. In this paper, Object recognition is possible with help of YOLO (you only look once) which is one of the real time CNN methods aims to detect object inside the image as part of road track. The result manifests the road lane detection guidance and object recognition along with prediction probability and bounding box.

Keywords: Convolution Neural Network, Hough Transform, Lane Detection, Object Recognition, YOLO.
Scope of the Article: Computer Vision

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G10040597S20

IJITEE Journal — Fri, 18 Aug 2023 09:17:05 +0000

Fake Information Detection Techniques
Henil Chopada¹, Maitri Patel², Rushikesh Desai³, Divya Ebenezer Nathaniel⁴
¹Henil Chopada, Student, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.

²Maitri Patel, Student, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.

³Rushikesh Desai, Student, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.

⁴Mrs. Divya Ebenezer Nathaniel, Assistant Professor, Department of Computer Science and Engineering, Babaria Institute of Technology, Vadodara (Gujarat), India.

Abstract: Fake or unverified information spreads same as actual facts on the internet, hence maybe going viral and influencing the general public opinion and their choices. fake news represents the maximum present-day forms of fake and unverified facts, respectively, and need to be detected as soon as possible for averting their results. The interest in efficient detection techniques has been therefore growing very rapid in the remaining years. In this paper we present survey on the specific techniques to computerized detection of fake news proposed in the latest literature. Particularly, this paper focus on five main aspects. First, we record and discuss the various definitions of fake news that have been considered in various literatures. Second, we highlight how the collection of applicable data for simulation of fake information detection is tough and we present the numerous approaches, which have been adopted to accumulate this information, additionally the publicly available datasets. Third, we describe the features that have been used in various fake news detection techniques. Fourth, we provide an evaluation of various techniques used for detection. In the end, we discuss future directions that could be considered for this problem.

Keywords: Data Mining, Text Mining, Machine Learning, Deep Learning, Natural Language Processing, Classification, Fake News.
Scope of the Article: Community Information Systems

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G10250597S20

IJITEE Journal — Fri, 18 Aug 2023 08:56:42 +0000

Gesture-Based Robot Control with Kinect Sensor
Bhatt Meet¹, Joshi Hari², Vaghasiya Denil³, Rohit R Parmar⁴, Pradeep M Shah⁵
¹Bhatt Meet, UG Student, Department of Electronics and Communication, G. H. Patel College of Engineering & Technology, Vallabh-Vidyanagar (Gujarat), India.

²Joshi Hari, UG Student, Department of Electronics and Communication, G. H. Patel College of Engineering & Technology, Vallabh-Vidyanagar (Gujarat), India.

³Vaghasiya Denil, UG Student, Department of Electronics and Communication, G. H. Patel College of Engineering & Technology, Vallabh-Vidyanagar (Gujarat), India.

⁴Rohit R Parmar, Assistant Professor, Department of Electronics and Communication, G. H. Patel College of Engineering & Technology, Vallabh-Vidyanagar (Gujarat), India.

⁵Pradeep M Shah, Assistant Professor, Department of Electronics and Communication, G. H. Patel College of Engineering & Technology, Vallabh-Vidyanagar (Gujarat), India.

Abstract: With the recent improvement in technology, controlling a robot wirelessly has become possible. Controlling a robot wirelessly increases its mobility. It also opens up the possibility of different kinds of robot designs. However, controlling a robot wirelessly using human computer interface bring more challenges to the researchers. This paper implements a human gesture-based robot control system. It uses a Kinect sensor which consists of a depth sensor, RGB camera. The Kinect sensor obtains the skeletal data from the subject. Using mathematical calculation, it calculates the angle deviation between the different parts of the arm based on its movement. The angle deviation data measured by the Kinect sensor will be transmitted by using a Wi-Fi module. The use of the Wi-Fi module will increase mobility. On the receiver, we use a microcontroller which will convert the received angle deviations values into equivalent PWM which will be used to control the servo motors. The combination of the different servo motors will result in the robotic movement.

Keywords: Kinect, Skeleton Tracking, Raspberry Pi 3B, Servo Motors, Hardware, Human Computer Interface.
Scope of the Article: Robotics and Control

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G10240597S20

IJITEE Journal — Fri, 18 Aug 2023 08:00:34 +0000

Regression Model Method for Analyze the Association Rules using Major Parameters
G. G. Shah¹, H. N. Patel²
¹G. G. Shah, Faculty of Business Administration, Dharmsinh Desai University, Nadiad, India.

²Dr. H. N. Patel, Department of Computer Science, Dr. Babasaheb Ambedker Open University, Ahmedabad, India.

Abstract: Using the data mining user can extract the information. Frequent itemsets is one of the popular task in data mining. Association Rule Analysis is the task of discovering association rules that occur frequently in a given large data set.The task is to find certain relationships among a set of itemsets in the database. There are two fundamental parameter(measurement) is Support and Confidence.Traditional association rule mining techniques employ predefined support and confidence values. But, it’s observed that specifying minimum support value of the minded rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system.This paper proposes a non-linear regression model using support, confidence and association rules. To predict the number of rules under the given explanatory variables say parameters. Use the R language for the Rules generations and also uses significance test to verify regression coefficients. Using the coefficient test and F-test verify the model.

Keywords: Association Rules, Regression, Regression Coefficients, Multiple Correlation, F-Test.
Scope of the Article: Open Models and Architectures

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