http://localhost:8080/xmlui/handle/123456789/3792 2026-06-23T06:30:09Z http://localhost:8080/xmlui/handle/123456789/3802 Title: EMERGING AI-ENABLED SECURITY FOR INDUSTRY 4.0 Authors: Dabbe, Chetan; Rakibe, Priya; Agarwal, Nimish; Barhate, Bhavesh; Choudhari, Rucha; Pawar, Sakshi Abstract: In the world of industry, where everything is going to be connected and automated. The expanding network of interconnected devices and systems heightens their exposure to cyber-attacks and security breaches. For solving such problems or to overcome such problems, combining AI with cybersecurity is essential, because AI is mostly used for analyzing the huge amount of data from sensors and devices to detect and prevent cyber threats. The use of Machine Learning algorithms is crucial for recognizing regular behavioral patterns and detecting any deviations that may indicate potential security threats. Anomaly detection, predictive analysis and Intrusion detection are the various techniques that are used in combination with cybersecurity measures i.e. intrusion detection system and firewalls for providing the best approach to cybersecurity for Industry applications. Additionally, the ideal AI-driven cybersecurity solution integrates advanced technologies for real-time anomaly detection and comprehensive security response, ensuring proactive protection against evolving cyber threats. Moreover, it seamlessly integrates with existing security infrastructure, enhancing overall security posture and facilitating a unified approach to threat detection and response. The main aim to use AI with cybersecurity for industry application is to reduce the increasing cyber-attacks. 2023-12-05T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3800 Title: Ramrajya in Kaliyug: Finding the Inner Ram with Modern Astra 2023-11-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3798 Title: Review on ML for recognition of Chat - GPT generated text Authors: Dixit, Ishan Swapnil; Rakibe, Priya 2024-01-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3795 Title: Combining Multiple Feature Extraction and Classification Methods to Study Performance of Handwritten Sanskrit Character Recognition Authors: Shelke, Shraddha V; Chandwadkar, D.M.; Ugale, S. P. Abstract: The recognition of Sanskrit handwriting has been found to be one of the most challenging research topics. The Sanskrit language is written using the Devanagari Script. In this paper, we implemented novel algorithm to recognize handwritten characters using four different feature extraction methods, resizing the image, Canny Edge detection, Hybrid Discrete Wavelet-Discrete Cosine Transform (DWT-DCT), Histogram of oriented Gradients (HOG). Classification is done by using support vector machine with cubic kenrel and neural network with ReLU activation function. When features are extracted by HOG, the SVM provides classification accuracy of 97.10% while the neural network provides 91.40%. SVM has been found to provide superior classification than neural networks for all feature extraction strategies. 2023-08-18T00:00:00Z