Volume-11 Issue-6, March 2023 – International Journal of Recent Technology and Engineering (IJRTE)

F74890311623

IJRTE Journal — Sat, 25 Mar 2023 12:07:31 +0000

Traffic Signal Scheduling using Machine Learning
Onkar Nichat¹, Suraj Kulkarni², Aditya Mane³, Siddhesh Naik⁴, Shubham Bhandari⁵
¹Mr. Onkar Nichat, Department of Computer Science and Engineering, Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
²Mr. Suraj Kulkarni, Department of Computer Science and Engineering, Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
³Mr. Aditya Shripad Mane, Department of Computer Engineering, JSPM’s Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
⁴Mr. Siddhesh Naik, Department of Computer Engineering, JSPM’s Imperial College of Engineering and Research, Wagholi, Pune (Maharashtra), India.
⁵Mr. Shubham Bhandari, Assistant Professor, Jayawant Shikshan Prasarak Mandal’s Imperial college of engineering and research, Wagholi, Pune (Maharashtra), India.
Manuscript received on 20 February 2023 | Revised Manuscript received on 14 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 111-116 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74890311623 | DOI: 10.35940/ijrte.F7489.0311623

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© 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: Within the past few years, the number of vehicles increased drastically, and therefore, the traffic of cars became a significant issue in urban as well as rural areas. Considerable traffic is happening in the area where many roads intersect with each other. Our existing traffic signal is not real-time and operates according to its pre-programmed settings, regardless of traffic conditions. To avoid traffic, traffic signals should give priority to the road with the highest vehicle density. By doing this, we can accommodate the maximum number of vehicles within a specific period. This type of signal responds to real-time situations and makes informed decisions in response. Hence, this system is also known as an intelligent traffic light system. The purpose of this study is to get the traffic situation on the roads in real-time and act accordingly. Using a web camera that should be mounted on the signals, we can obtain real-time footage of the streets. By applying image processing methods, we can determine the vehicle densities on each road. Signals that are programmed priorly to or incorrect signal scheduling were found to play the most significant role in causing vehicle traffic. This innovative traffic signal scheduling system is a superior option compared to existing systems, as it makes decisions based on real-time traffic conditions.

Keywords: Image recognition, Machine Learning, Image Processing.
Scope of the Article: Machine Learning

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F74980311623

IJRTE Journal — Sat, 25 Mar 2023 12:01:51 +0000

Implementation of LVCMOS based 4 Bit FPGA Based ALU on SP 701 Board for New Digital Age Technologies
Chandrashekhar Patel¹, Abhay Saxena², Anita Rawat³, Omprakash Nautiyal⁴
¹Dr. Chandrashekhar Patel, Department of Computer Science, Dev Sanskriti Vishwavidyalaya, Haridwar (Uttarakhand), India.
²Prof. Abhay Saxena, Dean, School of Technology, Management & Communication, Dev Sanskriti Vishwaviidyalya, Hardwar (Uttarakhand), India.
³Prof. (Dr.) Anita Rawat, Director, Uttarakhand Science Education and Research Centre (USERC), Dehradun (Uttarakhand), India.
⁴Prof. Omprakash Nautiyal, Uttarakhand Science Education & Research Centre (USERC) Dehradun (Uttarakhand), India.
Manuscript received on 24 February 2023 | Revised Manuscript received on 14 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 102-110 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74980311623 | DOI: 10.35940/ijrte.F7498.0311623

Abstract: Objectives: The 4-bit ALU of a RISC processor is designed as shown by the researcher in this paper. The 4-bit ALU used in this work can perform 24 = 16 various arithmetic and logical operations, including addition, subtraction, multiplication, and division, as well as logical AND, OR, NAND, NOR, NOT, XOR, XNOR, INCREMENT, DECREMENT, ROTATE LEFT, and ROTATE RIGHT. Methods: The author used the Vivado simulation tools with the Verilog HDL language to build the FPGA-based ALU, and the SP701 Spartan FPGA board was used to implement the entire design. It has been implemented to use energy-efficient IO standard approaches. Findings: By calculating the overall power usage at the pre- and post-levels, this research has developed a new method for building energy-efficient FPGA-based ALUs. The author utilised the Vivado simulation tool for this investigation. The SP701 FPGA board has also been used to implement this idea. Novelty: The Internet of Things and other emerging digital era technologies will undoubtedly benefit from this research work, and its energy-efficient design will support environmental initiatives.

Keywords: SP701, FPGA, VIVADO, RISC, LVCMOS, Verilog
Scope of the Article: FPGAs

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F75060311623

IJRTE Journal — Fri, 24 Mar 2023 07:49:06 +0000

Smart Accident Detection and Emergency Notification System with GPS and GSM Integration
Rajat Amat¹, Sunil Mallick², Priyanka Suna³
¹Rajat Amat, Department of Electronics and Communication Engineering, Sambalpur University Institute of Information Technology, Jyoti Vihar, Burla (Odisha), India.
²Sunil Mallick, Department of Electronics and Communication Engineering, Sambalpur University Institute of Information Technology, Jyoti Vihar, Burla (Odisha), India.
³Priyanka Suna, Department of Electronics and Communication Engineering, Sambalpur University Institute of Information Technology, Jyoti Vihar, Burla (Odisha), India.
Manuscript received on 28 February 2023 | Revised Manuscript received on 13 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 97-101 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F75060311623 | DOI: 10.35940/ijrte.F7506.0311623

Abstract: In the aftermath of an incident, numerous valuable lives are tragically forfeited due to the lack of timely and adequate emergency response services. Hence, there arises a necessity for a device to be installed in vehicles that can detect any untoward incidents and promptly notify the nearest emergency service providers, such as hospitals, ambulance services, and police stations. This paper outlines the development of an Internet of Things (IoT)-based device that comprises an Arduino UNO R3 microcontroller, an accelerometer sensor, a GPS module, and a GSM module. Real-time information about the accident’s location, time, and date is sent using GPS and communicated to the emergency service provider through GSM. Upon receiving this crucial information, the emergency service provider can promptly take appropriate action, thereby potentially saving the lives of numerous individuals affected by the accident.

Keywords: Internet of Things, GSM, GPS, Accident Detection
Scope of the Article: Internet of Things

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F74950311623

IJRTE Journal — Sat, 11 Mar 2023 09:38:12 +0000

Forecasting Student Clothes Purchases Intention in Bangladesh: A Machine Learning Approach
Md Mijanur Rahman¹, Md. Zahid Hasan², Md Golam Morshed³, Sanjida Karim⁴, Mamunur Rashid Alex⁵
¹Md. Mijanur Rahman, Assistant Professor, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
²Md. Zahid Hasan, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
³Md GolamMorshed, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
⁴Sanjida Karim, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
⁵Mamunur Rashid Alex, U.G Student, Department of Computer Science and Engineering, Southeast University, Dhaka, Bangladesh.
Manuscript received on 23 February 2023 | Revised Manuscript received on 06 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 91-96 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74950311623 | DOI: 10.35940/ijrte.F7495.0311623

Abstract: Online shopping provides an excellent opportunity and platform for today’s traditional businesses. Because of the advancement of online purchasing systems, students often prefer online shopping. Thus, students’ involvement in online purchasing has become a significant trend. The research aims to determine university students’ purchase intentions toward Bangladeshi clothing brands using several machine learning approaches. An online questionnaire survey was conducted among 1000 university students to understand their attitudes toward online shopping from a different perspective. This paper presents a comparative study of various machine-learning techniques applied to the problem of customer purchasing intention. The experiments were conducted using supervised machine learning techniques, such as linear regression, logistic regression, and Support Vector Machine (SVM), which was also used to predict university students’ purchase intentions. This study found that students’ age, quality of clothes, purchase discount, and price positively influenced their purchase intentions, while the buying risk negatively impacted these intentions. Linear regression achieves the highest accuracy with the maximum number of features, yielding an accuracy of 89.2%.

Keywords: Bangladeshi brands, linear regression, Logistic regression, Support vector machine (SVM), online shopping, purchase intention, Machine learning.
Scope of the Article: Machine learning

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F74820311623

IJRTE Journal — Sat, 11 Mar 2023 09:26:41 +0000

Intrusion Attacks on Deep Learning Frameworks Employed in Self-Driving Vehicles
Syeda Kausar Fatima¹, Syeda Gauhar Fatima²
¹Dr. Syeda Kausar Fatima, Department of Electronics and Communication, Deccan College of Engineering and Technology, Hyderabad (Telangana), India.
²Dr. Syeda Gauhar Fatima, Department of Electronics and Communication, Deccan College of Engineering and Technology, Hyderabad (Telangana), India.
Manuscript received on 18 February 2023 | Revised Manuscript received on 01 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 84-90 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74820311623 | DOI: 10.35940/ijrte.F7482.0311623

Abstract: Deep convolutional networks have proven practical for autonomous vehicle applications as deep CNN technology has advanced. There has been a growing vogue for using end-to-end computational methods for the mechanization of vehicular activities. Preliminary studies, though, have demonstrated that deep learning network classifiers are sensitive to adversarial approaches. However, the impact of adversarial strategies on regression problems remains poorly understood. We propose two white-box direct security breaches targeting progressive selfdriving vehicles in this research. A prediction model is used in the navigation mechanism, which receives a picture as input and returns a steering angle. By altering the input image, we may influence the actions of the automated driving unit. Two different attacks can be launched in practice on CPUs without the need for GPUs. The effectiveness of the threats is demonstrated by trials carried out in Udacity.

Keywords: Adversarial Intrusions, Driverless Vehicles, Nvidia’s Driving Architecture, Regression Model.
Scope of the Article: Classification

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F74960311623

IJRTE Journal — Sat, 04 Mar 2023 10:41:07 +0000

Design and Fabrication of Mems U-Shaped Cantilever
K.Durga Aparna¹, K. L. V. Nagasree², G. Lalitha Devi³
¹Dr. K. Durga Aparna, Department of Electronics, PRISM Degree College Andhra University, Visakhapatnam (A.P), India.
²K. L. V. Nagasree, Department of Physics, Andhra University, Visakhapatnam (A.P), India.
³G. Lalitha Devi, Department of Mathematics, PRISM Degree College Andhra University, Visakhapatnam (A.P), India.
Manuscript received on 24 February 2023 | Revised Manuscript received on 03 March 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 80-83 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74960311623 | DOI: 10.35940/ijrte.F7496.0311623

Abstract: MEMS are used in acceleration, flow, pressure and force sensing applications on the micro and macro levels. The fundamental part of every sensor is the transducer, which converts the measured quantity of interest into an interpretable output signal. The most prominent transducer is the piezoresistive cantilever, which translates any signal into an electrical signal. This paper presents the design and fabrication of a U-shaped cantilever with enhanced sensitivity and stiffness, which yields better results than other cantilevers. The simulation results of the cantilevers are designed using COMSOL software. MEMS technology becomes more affordable, better, and easier to fabricate in increasing quantities. Each layer of the fabrication process is quite complex, and the final manufactured product will be tested and used for high-end applications.

Keywords: Mems, Fabrication, Cantilever, Comsol
Scope of the Article: Design and Diagnosis

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F74970311623

IJRTE Journal — Fri, 03 Mar 2023 13:14:39 +0000

A Review on Detection and Correction of Artifacts from EEG Data
Sagar Motdhare¹, Garima Mathur²
¹Dr. Sagar Motdhare, Assistant Professor, Indian Institute of Information Technology, Nagpur (Maharashtra), India.
²Dr. Garima Mathur, Professor, Poornima University, Jaipur (Rajasthan), India.
Manuscript received on 24 February 2023 | Revised Manuscript received on 28 February 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 74-79 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74970311623 | DOI: 10.35940/ijrte.F7497.0311623

Abstract: Electroencephalography (EEG) offers a wide range of uses in a variety of industries. Low SNR (signal-to-noise ratio), however, limits EEG applicability. A variety of artefacts cause EEG noise, and numerous strategies have already been developed to identify and eliminate these inconsistencies. Various methods differ from merely identifying and discarding artefact-ridden segments to isolating the noise content of the EEG signal. With an emphasis on the previous half decade, we discuss a range of contemporary and traditional strategies for EEG data artefact recognition and removal. We assess the merits and drawbacks of the approaches before proposing potential prospects for the area.

Keywords: Electroencephalography (EEG), Artifact, Artifact Removal, Artifact Correction
Scope of the Article: Signal Control System & Processing

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F74750311623

IJRTE Journal — Sat, 25 Feb 2023 12:38:20 +0000

Analyzing Students’ Attitudes and Behavior Toward Artificial Intelligence Technologies in Higher Education
Latifa Alzahrani
Latifa Alzahrani, Department of Management Information Systems, College of Business Administration, Taif University, Saudi Arabia.

Abstract: In this study, we aim to contribute to the existing literature on the implementation of artificial intelligence (AI) in education. We examine the factors that influence students’ behaviour and attitudes toward the use of AI in higher education. We employed a quantitative approach using a wide range of adoption theories and models, including the unified theory of acceptance and use of technology model. We formulated hypotheses and verified the conceptual model. A questionnaire was used to collect data from 350 students. The structural equation model (SEM) was applied to estimate the relationship between dependent and independent variables. Based on SEM results, we found that despite perceived risk negatively impacting students’ attitudes, the factors of performance expectancy and facilitating conditions significantly influenced students’ attitudes and their behavioural intention to use AI in education. The results also show that effort expectancy does not considerably influence attitudes toward AI use in higher education. Research limitations are discussed at the end of this study.

Keywords: Artificial Intelligence; Education; Students’ Attitude; Behavior
Scope of the Article: Artificial Intelligence

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F74910311623

IJRTE Journal — Sat, 25 Feb 2023 11:12:36 +0000

Cybercrime Landscape and Changes During the Pandemic in Division 4 (West North Central)
Trang Thi Thu Horn¹, Mahmoud Yousef²
¹Trang Thi Thu Horn, Department of Computer Science and Cybersecurity, College of Health Science and Technology, University of Central Missouri, Warrensburg, USA.
²Dr. Mahmoud Yousef, Department of Computer Science and Cybersecurity, College of Health Science and Technology, University of Central Missouri, Warrensburg, USA.
Manuscript received on 20 February 2023 | Revised Manuscript received on 24 February 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 59-64 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74910311623 | DOI: 10.35940/ijrte.F7491.0311623

Abstract: Since its existence in December 2019, COVID-19 has significantly impacted different sectors of the economy in both the physical and digital worlds. In cyberspace, the COVID-19 pandemic has changed the cybercrime landscape. In this study, we focus on data collected by the Federal Bureau of Investigation (FBI) in the West North Central (Division 4) region. Data was collected for each state for different types of crimes and was divided into various age groups. Non-payment and non-delivery have the highest number of victims compared to other kinds of crimes. Moreover, over the past five years, the over-60 age group had the highest number of victims in Kansas, Missouri, South Dakota, and Minnesota. However, the number of elderly cybercrime victims ranked second compared to other age groups in Iowa and Nebraska and third in North Dakota.

Keywords: Cybercrimes, Elderly Fraud, COVID-19 Impact.
Scope of the Article: Security, Privacy and Trust in IoT & IoE

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F74760311623

IJRTE Journal — Sat, 25 Feb 2023 11:07:58 +0000

Sarcasm Detection Using Deep Learning Approaches: A Review
Spriha Sinha¹, Monika Choudhary²
¹Spriha Sinha, Department of Computer Science and Engineering – Artificial Intelligence, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India.
²Monika Choudhary, Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India.
Manuscript received on 13 February 2023 | Revised Manuscript received on 21 February 2023 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 50-58 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.F74760311623 | DOI: 10.35940/ijrte.F7476.0311623

Abstract: Emotions are something that makes one realize how other people are feeling but sarcasm needs to be understood by putting in some extra effort. Sarcasm, a verbal irony, is a practice of using words or sentences that are different from their literal meaning. Researchers are still working to develop an algorithm that can accurately identify sarcasm. Since sometimes humans also take time to understand sarcasm, making a machine learn to recognize is also not a simple task. The need for Deep Learning (DL) is rapidly growing for detection and classification operations. Different research works have focused on Sarcasm detection using various methodologies; however, the issue with existing research is its limited performance and accuracy. Our survey provides several helpful examples, the most notable of which is a table that lists prior studies according to several criteria, including the types of methodologies used with accuracy and the datasets employed. This paper also sheds light on multimodal detection, sarcasm detection from typographic images (such as memes), feature set analysis, and the various phases of a model, including its issues and milestones in sarcasm detection.

Keywords: Sarcasm Detection, Deep Learning, Sentiment, Social Media, Typographic
Scope of the Article: Deep Learning

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