Volume-3 Issue-6, October 2023 – Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) https://www.ijainn.latticescipub.com Exploring Innovation | ISSN: 2582-7626 (Online) | A Periodical Journal | Reg. No: C/1383209 | Published by Lattice Science Publication (LSP) Wed, 27 Mar 2024 12:27:35 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 https://www.ijainn.latticescipub.com/wp-content/uploads/2021/01/cropped-IJAINN-logo-WF-32x32.png Volume-3 Issue-6, October 2023 – Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) https://www.ijainn.latticescipub.com 32 32 F95170512623 https://www.ijainn.latticescipub.com/portfolio-item/f95170512623/ Wed, 27 Mar 2024 12:26:57 +0000 https://www.ijainn.latticescipub.com/?post_type=portfolio&p=2143 The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

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]]> The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Mitigating Peak Sidelobe Levels in Pulse Compression Radar using Artificial Neural NetworksCROSSMARK Color horizontal
K. Raja Rajeswari1, N. Roopa Vathi2, B. Vijaya Lakshmi3, P. V. K. Chaitanya4

1Raja Rajeswari Konduri, Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam (A.P), India.

2Nagireddi Roopavathi, Assistant Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam (A.P), India.

3Balantrapu Vijaya Lakshmi, Associate Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam (A.P), India. 

4Putrevu Venkata Krishna Chaitanya, Assistant Professor, Department of Electronics and Communication Engineering, GVP College of Engineering for Women, Visakhapatnam (A.P), India.

Manuscript received on 06 April 2023 | Revised Manuscript received on 15 April 2023 | Manuscript Accepted on 15 October 2023 | Manuscript published on 30 March 2024 | PP: 12-20 | Volume-3 Issue-6, October 2023 | Retrieval Number: 100.1/ijainn.F95170512623 | DOI: 10.54105/ijainn.F9517.03061023

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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper, Artificial Neural Networks (ANNs) are being considered to obtain low sidelobe pattern for binary codes and thereby to improve the performance of pulse compression radar. Pulse compression is a popular technique used for improving range resolution in the radar systems. This paper proposes a new approach for Pulse Compression using various types of ANN networks like Multi-Layer Perception (MLP), Recursive Neural Networks (RNN), Radial Basis Function (RBF) and Recurrent Radial Basis Function (RRBF) and a special class of Feed-Forward Wavelet Neural Network (WNN) with one input layer, one output layer and one hidden layer are being considered. Networks of 13-bit Barker code and extended binary Barker codes of 35, 55 and 75 length codes were used for the implementation and thereby to improve the performance of pulse compression radar. WNN-based networks using Morlet and Sigmoid activation function in hidden and output layers respectively, a special class of Artificial Neural Network is considered in this paper. The performance metrics used are Peak Sidelobe Ratio (PSLR), Integrated Sidelobe Ratio (ISLR) and Signal-to-Sidelobe Ratio (SSR). Further the performance in terms of range and Doppler resolution is also presented in this paper. Better performance in terms of sidelobe reduction can be achieved with ANNs compared to Autocorrelation Function (ACF) called as matched filter. If the sidelobe values are high there is possibility of masking weaker return signals and there by detection becomes difficult. From this paper it can be established that RRBF gives better result than other ANN networks. Further, WNN gives the best performance even compared with RRBF in terms of sidelobe reduction in pulse compression radar.

Keywords: ACF, ANNs, WNN, PSLR, ISLR, SSR, Pulse Compression, Barker code, MLP, RNN, RBF, RRBF, Range resolution, Doppler resolution, Matched Filter
Scope of the Article: Neural Networks

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]]> F1072103623 https://www.ijainn.latticescipub.com/portfolio-item/f1072103623/ Sat, 28 Oct 2023 10:32:19 +0000 https://www.ijainn.latticescipub.com/?post_type=portfolio&p=2048 The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

The post F1072103623 appeared first on Indian Journal of Artificial Intelligence and Neural Networking (IJAINN).

]]> The Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) has ISSN 2582-7626 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in February, April, June, August, October and December.The aim of the journal is to publish high quality peer–reviewed original articles in the area of Artificial Intelligence and Neural Networking that covers Neural Networks, Fuzzy Logic, Simulated Biological Evolution Algorithms (Like Genetic Algorithm), Ant Colony Optimization, Reasoning and Evolution, Intelligence Applications, Computer Vision and Speech Understanding, Multimedia and Cognitive Informatics, Data Mining and Machine Learning Tools, Heuristic and Ai Planning Strategies and Tools, Computational Theories of Learning, Technology and Computing (Like Particle Swarm Optimization), Intelligent System Architectures, Knowledge Representation, Bioinformatics, Natural Language Processing, Multiagent Systems, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data and AI Approaches, Reinforcement Learning, Learning with Generative Adversarial Networks, Regression and Prediction, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Vision and Speech Perception, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Reasoning and Inference, Marketing and Social Sciences, Knowledge Discovery, Web Mining, Information Retrieval, Design and Diagnosis, Game Playing, Streaming Data, Music Modelling and Analysis, Robotics and Control, Multi-Agent Systems. #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and AI Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and AI Approaches #Reinforcement Learning #Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining, Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent systems #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Investigation of an Autonomous Vehicle’s using Artificial Neural Network (ANN)CROSSMARK Color horizontal
Abel Varghese1, Mahendher Marri2, Sibi Chacko3

1Abel Varghese, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.

2Mahendher Marri, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.

3Dr. Sibi Chacko, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK.

Manuscript received on 21 September 2023 | Revised Manuscript received on 29 September 2023 | Manuscript Accepted on 15 October 2023 | Manuscript published on 30 October 2023 | PP: 1-11 | Volume-3 Issue-6, October 2023 | Retrieval Number: 100.1/ijainn.F1072103623 | DOI: 10.54105/ijainn.F1072.103623

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© The Authors. Published by Lattice Science Publication (LSP). 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 presents a study of autonomous vehicles on a normal highway in UAE, using MATLAB/Simulink® 2022. There is great potential for autonomous vehicles to improve the safety and efficiency of transportation, enhance the quality of time spent in cars, and make transportation more accessible to everyone. The vehicles utilized in the study have three different speeds 40, 80, and 120 km/hr. All vehicles modelled are representative of that available in the UAE. In the model, lane following and lane-keeping assistance functions and Simulink block which are described using artificial neural networks are selected. Simulation is validated with existing published results of physical vehicle models. In the simulations, it is assumed that vehicles have minimal steering angles as the system is in an autonomous collision free environment, selected from MATLAB. Results are obtained as velocities, accelerations, and safe distance with respect to the preceding vehicle. The following results are critically analyzed and validated.
Nomenclature 𝑪𝒇 – Cornering stiffness of front tires, 𝑵/𝒓𝒂𝒅
𝑪𝒓 – Cornering stiffness of front tires, 𝑵/𝒓𝒂𝒅
𝒍𝒇 – Longitudinal distance from CG to front tires, 𝒎
𝒍𝒓 – Longitudinal distance from CG to rear tires, 𝒎
𝒎 – Total mass of the vehicle, 𝒌𝒈
𝑰𝒛 – Yaw moment of inertia of vehicle, 𝒎𝑵𝒔𝟐

Keywords: Artificial Neural Networks, Ackermann, Autonomous Vehicle, Deep Neural Networks, CNN, MATLAB, YOLO.
Scope of the Article: Neural Networks

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