Volume-2 Issue-4, June 2022 – 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) Fri, 18 Aug 2023 06:56:33 +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-2 Issue-4, June 2022 – Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) https://www.ijainn.latticescipub.com 32 32 D1054062422 https://www.ijainn.latticescipub.com/portfolio-item/d1054062422/ https://www.ijainn.latticescipub.com/portfolio-item/d1054062422/#respond Sat, 28 May 2022 07:12:26 +0000 https://www.ijainn.latticescipub.com/?post_type=portfolio&p=903 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 D1054062422 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

Removal of Image Steganography using Generative Adversarial NetworkCROSSMARK Color horizontal
Ritvij Vejare1, Abhishek Vaish2, Kapish Singh3, Mrunali Desai4
1Ritvij Vejare, Department of Computer Engineering, K.J. Somaiya Institute of Engineering and Information Technology, Mumbai (Maharashtra), India.

2Abhishek Vaish, Department of Computer Engineering, K.J. Somaiya Institute of Engineering and Information Technology, Mumbai (Maharashtra), India.

3Kapish Singh, Department of Computer Engineering, K.J. Somaiya Institute of Engineering and Information Technology, Mumbai (Maharashtra), India.

4Mrunali Desai, Assistant Professor, Department of Computer Engineering, K.J. Somaiya Institute of Engineering and Information Technology, Mumbai (Maharashtra), India.

Manuscript received on 10 May 2022 | Revised Manuscript received on 24 May 2022 | Manuscript Accepted on 15 June 2022 | Manuscript published on 30 June 2022 | PP: 6-10 | Volume-2 Issue-4, June 2022 | Retrieval Number: 100.1/ijainn.D1054062422 | DOI: 10.54105/ijainn.D1054.062422

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Secret messages can be concealed in ordinary media like audio, video and images. This is called as Steganography. Steganography is used by cyber attackers to send malicious content that could harm victims. Digital steganography, or steganography in photographs, is exceedingly difficult to detect. The detection of steganography in images, has been investigated in thoroughly by a variety of parties. The use of steganographic techniques to send more malware to a compromised host in order to undertake different post-exploitation operations that affect the exploited system. Many steganalysis algorithms, on the other hand, are limited to working with a subset of all potential photos in the wild or have a high false positive rate. As a result, barring any suspected image becomes an arbitrary policy. Filtering questionable photos before they are received by the host machine is a more practical policy. In this paper, a Generative Adversarial Network based model is proposed that may be optimized to delete steganographic content while maintaining the original image’s perceptual quality. For removing steganography from photos while keeping the maximum visual image quality, a model is built utilizing a combination of Generative Adversarial Network (GAN) and Image Processing. In the future, utilizing a generator to synthesize a picture will become more popular, and detection of steganography in images will become very difficult. In comparison to other models that have been addressed further, the proposed model is able to give a mean square error of 5.4204 between the generated image and the cover image, as well as better outcomes based on several metrics. As a result, a GAN-based steganography eradication method will aid in this endeavor.

Keywords: Removal of Steganography, Steganalysis, Generative Adversarial Network, Neural Network, Deep Learning
Scope of the Article: Deep Learning

Download PDF

The post D1054062422 appeared first on Indian Journal of Artificial Intelligence and Neural Networking (IJAINN).

]]> https://www.ijainn.latticescipub.com/portfolio-item/d1054062422/feed/ 0 D1052062422 https://www.ijainn.latticescipub.com/portfolio-item/d1052062422/ https://www.ijainn.latticescipub.com/portfolio-item/d1052062422/#respond Sat, 21 May 2022 07:45:13 +0000 https://www.ijainn.latticescipub.com/?post_type=portfolio&p=900 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 D1052062422 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

Stock Price Prediction using LSTMCROSSMARK Color horizontal
Sakshi Vora1, Rayees Shaikh2, Kartik Bhanushali3, Pradnya Patil4
1Sakshi Vora, Department of Information Technology, K. J. Somaiya Institute of Engineering & Information Technology, Mumbai (Maharashtra), India.

2Rayees Shaikh, Department of Information Technology, K. J. Somaiya Institute of Engineering & Information Technology, Mumbai (Maharashtra), India.

3Kartik Bhanushali, Department of Information Technology, K. J. Somaiya Institute of Engineering & Information Technology, Mumbai (Maharashtra), India.

4Prof. Pradnya Patil, Department of Information Technology, K. J. Somaiya Institute of Engineering & Information Technology, Mumbai (Maharashtra), India.

Manuscript received on 03 May 2022 | Revised Manuscript received on 19 May 2022 | Manuscript Accepted on 15 June 2022 | Manuscript published on 30 June 2022 | PP: 1-5 | Volume-2 Issue-4, June 2022 | Retrieval Number: 100.1/ijainn.D1052062422 | DOI: 10.54105/ijainn.D1052.062422

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: The movement of stock prices is non-linear and complicated. In this study, we compared and analyzed various neural network forecasting methods based on real problems related to stock price demand forecasting. We ultimately selected the LSTM (Long Short-Term Memory) [1] neural network as traditional RNN’s long-term reliance is improved by LSTM, which substantially enhances prediction accuracy and stability. The practicality of this method and the pertinence of the model are then inspected, and final conclusions are drawn through a detailed examination of stock price forecasts using LSTM neural networks optimized by RNN algorithms. Past information has proven to be extremely predominant to investors as the basis for financing resolution. Previous studies have used open and close prices as vital predictors of financial markets, but utmost highs and lows may provide extra information regarding future price actions. Hence, two representative stock indexes of the Indian stock market were selected for the survey, and the main data collected from them are open, closed, lowest, highest, date, and everyday transaction size. The outcome shows that the LSTM model has few restraints, including a forecast time lag, but you can use the attention level to foretell stock prices. Its primary idea is to analyze stock market historical information and find the role of time series by digging deeper into its central rules.

Keywords: LSTM, Neural Network, Prediction, Stock Price.
Scope of the Article: Data Mining and Machine Learning Tools

Download PDF

The post D1052062422 appeared first on Indian Journal of Artificial Intelligence and Neural Networking (IJAINN).

]]> https://www.ijainn.latticescipub.com/portfolio-item/d1052062422/feed/ 0