Volume-12 Issue-2, July 2023 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org Exploring Innovation | ISSN:2277-3878(Online) | A Periodical Journal | Reg. No.: C/819981 | Published By BEIESP Tue, 24 Jun 2025 12:53:04 +0000 en-US hourly 1 https://www.ijrte.org/wp-content/uploads/2021/02/cropped-IJRTE-Logo-WS-32x32.png Volume-12 Issue-2, July 2023 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org 32 32 A75930512123 https://www.ijrte.org/portfolio-item/a75930512123/ Mon, 24 Jul 2023 10:16:21 +0000 https://www.ijrte.org/?post_type=portfolio&p=46886 The post A75930512123 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Link Prediction in Social Networks using Vertex Entropy
Shubham1, Rajeev Kumar2, Naveen Chauhan3
1Shubham, Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, (Himachal Pradesh), India.
2Dr. Rajeev Kumar, Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, (Himachal Pradesh), India.
3Dr. Naveen Chauhan, Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, (Himachal Pradesh), India.
Manuscript received on 07 June 2023 | Revised Manuscript received on 19 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 133-139 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.A75930512123 | DOI: 10.35940/ijrte.A7593.0712223

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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: Many link prediction methods have been put out and tested on several actual networks. The weights of linkages are rarely considered in these studies. Taking both the network’s structure and link weight into account is required for link prediction. Previous researchers mostly overlooked the topological structure of data in favour of the naturally occurring link weight. With the use of the concept of entropy, a new link prediction algorithm has been put forth in this paper. When applied in realtime social networks, this algorithm outperforms industrystandard techniques. This paper focuses on both topological structural information, which involves calculating the vertex entropy of each vertex and the link weight in the proposed method. Both weighted and unweighted networks can benefit from the proposed method. Unipartite and bipartite networks can also use the suggested methods. Furthermore, the results demonstrate that the proposed method outperforms competing and traditional strategies, particularly when targeted social networks are sufficiently dense.

Keywords: Ego Network, Social Network, Link Prediction, Sociogram.
Scope of the Article: Social Network

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]]> F75050311623 https://www.ijrte.org/portfolio-item/f75050311623/ Mon, 24 Jul 2023 10:11:11 +0000 https://www.ijrte.org/?post_type=portfolio&p=46882 The post F75050311623 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Autonomous Robot Navigation in Known Environment
Fazina Kosser1, Neerendra Kumar2
1Fazina Kosser, Department of Computer Science and Engineering, IT from Central University of Jammu, India.
2Neerendra Kumar, Department of Computer Science and Engineering, IT from Central University of Jammu, India
Manuscript received on 28 February 2023 | Revised Manuscript received on 11 July 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 128-132 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.F75050311623 | DOI: 10.35940/ijrte.F7505.0712223

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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: Autonomous robot navigation is one of the challenging researched topic in robotics. A secure and optimal path is required for any mobile robot navigation in a known environment. In this work, a Simulink model is proposed based on Pure Pursuit and path-following controllers for solving the navigation problem of mobile robots in a known environment. The Pure Pursuit controller is used to determine the linear and angular velocities of the robot. Moreover, the (x, y) coordinate position of the robot and t h e waypoints are input t o the pure pursuit block. Velocity commands are sent to drive the robot on the given path. The primary objective of the proposed model is to determine an obstacle-free path for mobile robot navigation. However, the robot must navigate from the start to the target location without hitting obstacles. For experimental results, the Turtle Bot Gazebo simulator is used. The “Robotic System Toolbox” in MATLAB is used to program the navigation process.

Keywords: Autonomous, Localization Motion Control, Navigation, Path Planning and Search Algorithms.
Scope of the Article: Autonomous Robots

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]]> F74620311623 https://www.ijrte.org/portfolio-item/f74620311623/ Mon, 24 Jul 2023 10:05:24 +0000 https://www.ijrte.org/?post_type=portfolio&p=46877 The post F74620311623 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Structural Topology Optimization of Headphone
Soutrik Mukherjee1, Kadambari R Vaikkat2
1Soutrik Mukherjee, Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, United States.
2Kadambari R Vaikkat, Department of Industrial Design, National Institute of Technology, Rourkela, India.
Manuscript received on 02 February 2023 | Revised Manuscript received on 11 July 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 114-127 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.F74620311623 | DOI: 10.35940/ijrte.F7462.0712223

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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: Topology optimization is a mathematical strategy enhancing a system’s performance by figuring out the best arrangement of materials for a particular set of loads, boundary conditions, and constraints. In basic terms, it builds a design space from a model (3D model). To make the design more efficient, it then eliminates or displaces material inside it. By defining cavities within continuous design domains, topology optimisation is an effective technique for generating lightweight, high-performance, and cost-effective structures. Like every other optimisation problem, it requires some boundary conditions, constraints, an objective function, and criteria to attain optimality, which must be determined by the type of design being made, material costs, mechanical performance, and resistance to failure. Since there are several iterations in the optimisation rounds, which allow us to experiment with variables within the boundary conditions to create an aesthetically pleasing, mechanically optimised design, we hope that the proper implementation of this will lead to the betterment of society.

Keywords: Material distribution, Mechanical performance Topology optimization.
Scope of the Article: Mechanical Maintenance

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]]> B78460712223 https://www.ijrte.org/portfolio-item/b78460712223/ Mon, 24 Jul 2023 10:00:16 +0000 https://www.ijrte.org/?post_type=portfolio&p=46873 The post B78460712223 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Named Entity Recognition (NER) and Relation Extraction in Scientific Publications
Anshika Singh1, Ankit Garg2
1Anshika Singh, Department of Computer Science Engineering, Abdul Kalam Technical University, Agra (U.P), India.
2Ankit Garg, Department of Computer Science Engineering, Abdul Kalam Technical University, Agra (U.P), India.
Manuscript received on 27 June 2023 | Revised Manuscript received on 07 July 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 110-113 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78460712223 | DOI: 10.35940/ijrte.B7846.0712223

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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: Scientific publications are essential sources of information for researchers across various fields. However, the increasing number of publications has made it challenging for researchers to keep up with the latest advancements. The task of extracting key phrases and relationships from scientific papers is of utmost importance in the field of natural language processing. This task plays a crucial role in helping researchers efficiently identify relevant articles and extract valuable insights from them. This research focuses on the problem of key phrase extraction, classification, and relationship identification in scientific publications. The problem is divided into two sub-problems: key phrase extraction and classification into PROCESS, TASK, and MATERIAL categories, as well as relationship identification. To address these sub-problems, advanced technologies such as SciBERT, MiniLM Sentence Transformer, and SVM are utilized. These techniques enable the efficient processing and analysis of scientific text, facilitating the extraction of key phrases, classification, and the identification of relationships. By effectively tackling these challenges, researchers can navigate the vast amount of scientific literature more efficiently, identifying relevant articles and uncovering valuable connections and insights within the text.

Keywords: Mini LM Sentence Transformer, Natural Language Processing, Sci BERT, SVM (Support Vector Machines).
Scope of the Article: Natural Language Processing

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]]> B78080712223 https://www.ijrte.org/portfolio-item/b78080712223/ Mon, 24 Jul 2023 09:54:16 +0000 https://www.ijrte.org/?post_type=portfolio&p=46869 The post B78080712223 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Classification of Emotion using Eeg Signals: an FPGA Based Implementation
Darshan B D1, Vyshnavi Shekhar B S2, Meghana M Totiger3, Priyanka N4, Spurthi A5
1Mr. Darshan B D, Department of Electronics and Communication Engineering, SJB Institute of Technology, Bangalore (Karnataka), India.
2Vyshnavi Shekhar B S, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
3Meghana M Totiger, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
4Priyanka N, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
5Spurthi A, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
Manuscript received on 07 June 2023 | Revised Manuscript received on 01 July 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 102-109 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78080712223 | DOI: 10.35940/ijrte.B7808.0712223

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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: An electroencephalograph is a device that records all electrical energy in the human brain using wearable metal electrodes placed on the skull. Electrical impulses connect brain cells and are always mobile, even at rest. This activity appears as a squiggly line in EEG recordings. Activity gaze data is pre-processed to a frequency range of 0 to 75 Hz. This creates a new matrix with a sample rate of 200 Hz and a range of 0-75 Hz. A finite-impulse-response low-pass filter was used because the bandpass would distort his EEG data after processing. Each pre-processed EEG signal has an output that completes the feature extraction process. Principal Component Analysis or PCA is passed in the feature reduction phase. PCA is an analytical process that uses singular value decomposition to transform a collection of corresponding features into mutually uncorrelated features or principal components. Principal component analysis: (a) mean normalization of features (b) covariance matrix (c) eigenvectors (d) reduced features or principal components. The above steps are passed to the SVM classifier for sentiment output. His VHDL code and testbench for 2*2 matrices were written, waveforms and RTL schemes were created in Xilinx 14.5. For the FPGA implementation, a Simulink model was designed, and the eigenvalues were pre-determined using a system generator.

Keywords: Electroencephalography (EEG), Autonomous Nervous System (ANS), Principal Component Analysis (PCA), Support Vector Machine (SVM).
Scope of the Article: Classification

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]]> B78230712223 https://www.ijrte.org/portfolio-item/b78230712223/ Mon, 24 Jul 2023 09:47:54 +0000 https://www.ijrte.org/?post_type=portfolio&p=46865 The post B78230712223 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Misalignment-Related Defect Detection using Discrete Wavelet Transform
Debayan Bhaumik1, Debrup Bhaumik2
1Debayan Bhaumik, Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal (Karnataka), India.
2Debrup Bhaumik, Department of Safety Engineering, National Institute of Technology, Rourkela (Odisha), India.
Manuscript received on 17 June 2023 | Revised Manuscript received on 22 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 97-101 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78230712223 | DOI: 10.35940/ijrte.B7823.0712223

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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: Induction motors are most commonly used in many industries, including petrochemicals, oil, and steel. A single failure in any of the induction motor’s components or subcomponents can result in a plant shutdown. The plant will suffer significant financial losses as a result. It is crucial to diagnose different types of faults in induction motors. Various condition monitoring techniques help diagnose faults in induction motors at an early stage. Vibration analysis is most commonly used among multiple condition monitoring techniques due to its higher accuracy compared to other methods. Vibration analysis is used to detect different types of faults in induction motors. The acceleration vibration data corresponding to multiple types of defects are gathered from publicly available web resources. The primary objective of this research work is to explore the severity of horizontal and vertical misalignment defects utilizing a signal processing approach. To achieve this objective, the Discrete Wavelet Transform (DWT) is used to detect abnormal behaviour in the induction motor. The Daubechies-4(db4) wavelet is chosen as a mother wavelet. As the Daubechies wavelet is an orthogonal wavelet, the percentage energy in all decomposed sub-bands will equal the original energy of the signal. The energy level of subbands is compared with the healthy condition of the motor to detect significant changes in motor fault.

Keywords: Induction motor, Vibration analysis, Discrete Wavelet Transform (DWT), Daubechies-4(db4)
Scope of the Article: Artificial Intelligence

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]]> A75300512123 https://www.ijrte.org/portfolio-item/a75300512123/ Mon, 24 Jul 2023 09:41:44 +0000 https://www.ijrte.org/?post_type=portfolio&p=46861 The post A75300512123 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Product Review Classification using Machine Learning and Statistical Data Analysis
Kajal Singh
Kajal Singh, Dartmouth College Hanover, NH, USA.

Manuscript received on 08 March 2023 | Revised Manuscript received on 21 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 91-96 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.A75300512123 | DOI: 10.35940/ijrte.A7530.0712223

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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: This paper aims to implement and analyse machine learning models for a product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The 5-fold cross-validation scores over the training data measure the performance of the models over each of the five classification tasks.

Keywords: Machine Learning, Classification, Clustering, Product
Scope of the Article: Clustering

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]]> B78090712223 https://www.ijrte.org/portfolio-item/b78090712223/ Mon, 24 Jul 2023 09:37:04 +0000 https://www.ijrte.org/?post_type=portfolio&p=46857 The post B78090712223 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Energy Efficient Data Aggregation Approach for Sensor Network using Trust Mechanism
Ayush Sharma1, Sunny Arora2
1Ayush Sharma, Department of Computer Science & Engineering, Guru Kashi University, Talwandi Sabo Bathinda (Punjab) India.
2Dr. Sunny Arora, Department of Computer Science & Engineering, Guru Kashi University, Talwandi Sabo Bathinda (Punjab) India.
Manuscript received on 11 June 2023 | Revised Manuscript received on 20 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 84-90 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78090712223 | DOI: 10.35940/ijrte.B7809.0712223

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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:  A wireless sensor network consists of numerous sensor nodes which are capable of entering and departing from the network according to their wish. The implementation of sensor networks is often done in remote locations, and the devices are small in size. Thus, energy consumption becomes the central issue of WSN. Due to the limited energy of sensor nodes, the data that is collected from the intended environment is sent directly to the central station. The sink node receives data from several sensor nodes. The decision-making process involves identifying and removing similarities between data from various sensor nodes. Additionally, the sink enables the local deployment of collected data and transmits it to remote networks. The existing research work utilises CTNR, an energy-efficient protocol that can enhance the duration of WSN. The CTNR protocol consists of two-level hierarchies for mitigating energy consumption in WSNs. This protocol facilitates the selection of cluster heads (CHs) based on distance and energy. This work will focus on enhancing the CTNR routing algorithm to prolong the network’s lifespan.

Keywords: IoT, WSN, CTNR, Routing, Multi-level Hierarchy
Scope of the Article: IoT

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]]> B78110712223 https://www.ijrte.org/portfolio-item/b78110712223/ Mon, 24 Jul 2023 09:32:06 +0000 https://www.ijrte.org/?post_type=portfolio&p=46853 The post B78110712223 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Development of Architecture for Digital Supply Chain (DSC) using Blockchain, IoT and AI Technologies
Rashmi Sharma1, Gauri Tripathi2, Pranjali Sharma3, Vani Tripathi4
1Dr. Rashmi Sharma, Assistant Professor, Department of Information Technology, Ajay Kumar Garg Engineering College, Ghaziabad (U.P), India.
2Gauri Tripathi, Student, Department of Mechanical Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India.
3Pranjali Sharma, Student, Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Prayagraj (U.P), India.
4Vani Tripathi, Student, Department of Mechanical Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India.
Manuscript received on 08 June 2023 | Revised Manuscript received on 20 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 77-83 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78110712223 | DOI: 10.35940/ijrte.B7811.0712223

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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: To synchronize data and transactions in a supply chain network, blockchain technology was developed. This study illustrates how blockchain technology can increase the adaptability and agility of supply chain operations. With the use of blockchain and other recently developed technologies, supply chain uncertainty and other industry-related problems can be addressed. Issues in the supply chain are categorised, and solutions are offered by integrating blockchain technology with the Internet of Things (IoT), Artificial Intelligence (AI), and other technologies. Integration demonstrates how tasks are completed utilising blockchain and various supply chain technologies. Digital supply chains(DSC) are setting standards in a wide range of sectors because they are effective and lean. The creation of sustainable and environmentally friendly supply chains benefits from the use of intelligent technology. The study paper covers the challenges of implementing a digital supply chain. This will serve as a foundation for studying supply chain technology.

Keywords: Smart Contracts, Digital Supply Chain(DSC) Operations, Blockchain Technology, Artificial Intelligence, Internet of Things
Scope of the Article: Internet of Things

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]]> B78070712223 https://www.ijrte.org/portfolio-item/b78070712223/ Mon, 24 Jul 2023 09:24:32 +0000 https://www.ijrte.org/?post_type=portfolio&p=46848 The post B78070712223 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Decentralized Storage for Educational Resources
Harsha R1, Indra E2, Thirugnana Sambandham3, K. Panimozhi4
1Harsha R, Department Computer Science and Engineering, B.M.S College of Engineering, Bangalore (Karnataka), India.
2Indra E, Department Computer Science and Engineering, B.M.S College of Engineering, Bangalore (Karnataka), India.
3Thirugnana Sambandham, Department Computer Science and Engineering, B.M.S College of Engineering, Bangalore (Karnataka), India.
4Dr. K. Panimozhi, Department Computer Science and Engineering, B.M.S College of Engineering, Bangalore (Karnataka), India.
Manuscript received on 07 June 2023 | Revised Manuscript received on 19 June 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 72-76 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78070712223 | DOI: 10.35940/ijrte.B7807.0712223

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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: The growth of blockchain over the last ten years has been astounding: from bitcoin to over 2,000 altcoins, and from decentralized electronic payments to programmable transactions, by complicated tokens and smart contracts controlled by autonomous entities. The technological aspects of blockchain are evolving simultaneously with the development of new blockchain applications. This paper considers one such sphere, developing a decentralised solution for accessing educational resources that will alleviate issues due to the single point of failure of centralised systems, democratise access to academic content, and provide a seamless experience for users who are not exposed to web3 technologies. This platform was developed by implementing Smart Contracts using Solidity and deploying them on the Ethereum blockchain to store educational resource metadata and files on the Inter Planetary File System (IPFS). Interfacing of blockchain and the frontend is done using web3.js. The platform is more robust and fault-tolerant than systems using a centralised architecture, and it provides a better user experience by incorporating a search mechanism for the files uploaded to the platform. This paper proposes a decentralised platform that allows users to upload educational resources that can be accessed by others, and implements a file metadata-based search feature to eliminate the need for users to remember the IPFS hash of a file.

Keywords: Ethereum blockchain, Decentralized Storage, Inter Planetary File System (IPFS), smart contract and Open Educational Resources (OER).
Scope of the Article: Artificial Intelligence

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