Informatics Publishing Limited

Mohd. Asif Gandhi ¹, S.M. Lekshmi Sri ², Bhanu Pratap Singh ³, Zahir Aalam ⁴, Subharun Pal ⁵

Affiliations
1 Department of Mechanical Engineering, Anjuman-I-Islam’s Kalsekar Technical Campus, IN
2 Department of Computer Science and Engineering, Dr. J.J. Magdum College of Engineering, IN
3 School of Information Technology, Auro University, IN
4 Department of Computer Engineering, Thakur College of Engineering and Technology, IN
5 Department of Computer Science and Engineering, Indian Institute of Technology, Jammu, IN

Abstract

Financial time series forecasting is crucial for making informed investment decisions. This study proposes an improvised method utilizing a Neuro-Fuzzy System (NFS) for enhanced forecasting accuracy. Traditional forecasting methods often struggle with the nonlinear and dynamic nature of financial time series data. NFS integrates neural network and fuzzy logic techniques, offering a robust framework for modeling complex relationships within financial data. The proposed method employs NFS to adaptively learn and model the intricate patterns present in financial time series data. It combines the strengths of neural networks in learning complex patterns and fuzzy logic in handling uncertainty and imprecision. This study contributes by introducing an innovative approach to financial time series forecasting, leveraging the capabilities of NFS to improve forecasting accuracy and reliability. Experimental results demonstrate the effectiveness of the proposed method in accurately forecasting financial time series data. The method outperforms traditional forecasting techniques, showcasing its potential for practical applications in financial markets.

Keywords

Financial Time Series Forecasting, Neuro-Fuzzy System, Forecasting Accuracy, Adaptive Learning, Complex Patterns

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M. Velammal ¹, Thiyam Ibungomacha Singh ², Nilesh Madhukar Patil ³, Subharun Pal ⁴

Affiliations
1 Department of Computer science and Technology, Karpagam College of Engineering, IN
2 Department of Computer Science and Engineering, Manipur Institute of Technology, Manipur University Campus, IN
3 Department of Computer Engineering, Dwarkadas Jivanlal Sanghvi College of Engineering, IN
4 Department of Computer Science and Engineering, Indian Institute of Technology Jammu, IN

Abstract

This paper introduces a novel approach for multiframe image restoration using Generative Adversarial Networks (GANs). Traditional image restoration techniques often struggle with handling complex degradation patterns and noise in images. In contrast, GANs have demonstrated remarkable capability in generating realistic and high-quality images. The proposed method leverages the power of GANs to restore multiframe degraded images by training the generator to learn the underlying clean image from a set of degraded frames. The discriminator collaborates with the generator to ensure the fidelity of the restored output. Experimental results on various datasets show that the proposed multiframe image restoration approach achieves superior performance compared to state-of-the-art methods in terms of image quality and fidelity.

Keywords

Multiframe, Image Restoration, Generative Adversarial Networks (GANs), Degradation Patterns, Fidelity.

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Dileep Pulugu ¹, P. Anusha ², Ratan Rajan Srivastava ³, R. Kalaivani ⁴, Subharun Pal ⁵

Affiliations
1 Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, IN
2 Department of Electronics and Communication Engineering, R.M.K. Engineering College, IN
3 Department of Computer Science and Engineering, B.N. College of Engineering and Technology, IN
4 Department of Computer Science, National College, IN
5 SSM Research Center, Swiss School of Management, CH

Abstract

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model's performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.

Keywords

Facial Recognition, Machine Learning, Video Surveillance, Deep Learning, Computer Vision

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Neerav Nishant ¹, Parul Saxena ², S.G. Surya ³, Ullal Akshatha Nayak ¹, Subharun Pal ⁴

Affiliations
1 Department of Computer Science and Engineering, Soban Singh Jeena University, IN
2 Department of Computer Science, Soban Singh Jeena University, IN
3 Department of Computer Science and Engineering, SCMS School of Engineering and Technology, IN
4 Department of Business Administration, Swiss School of Management, CH

Abstract

Cyberbullying has emerged as a pervasive issue in today's digitally connected society, with detrimental effects on individuals’ mental health and well-being. Despite increasing awareness and efforts to address cyberbullying, there remains a significant gap in utilizing wireless network security measures as a means of mitigation. The existing literature predominantly focuses on social and psychological aspects of cyberbullying, overlooking the potential role of wireless network security in prevention and intervention strategies. This research seeks to fill this gap by exploring the effectiveness of leveraging wireless network security to secure cyberspace against cyberbullying incidents. The research employs a multifaceted methodology, beginning with the estimation of expected rates and derivative risks of cyberbullying within wireless networks. These metrics are combined into a risk index value, which serves as a basis for prioritizing mitigation efforts. Additionally, the study explores the application of cyberspace modeling techniques, specifically Support Vector Machines (SVM), to enhance screening processes and identify potential cyberbullying incidents on Wireless Network Security (WNS). The findings of this research demonstrate the efficacy of integrating wireless network security measures into cyberbullying prevention strategies. By combining risk index values and leveraging SVM-based cyberspace modeling, the study identifies and prioritizes cyberbullying risks effectively. Furthermore, the implementation of wireless network security protocols contributes to a reduction in cyberbullying incidents, fostering safer digital environments for users.

Keywords

Cyberbullying, Wireless Network Security, Risk Assessment, Support Vector Machines (SVM), Prevention Strategies.

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