Dayal Chandra Sati

Chandigarh University

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Madan Lal Saini

Chandigarh University

Monika Saini

Maharshi Dayanand University

Ratish Kumar

Chandigarh University

Mahadev

Chandigarh University

Yogesh Misra

Mewar University

Sonali Dubey

Chandigarh University

Satyam Kumar

The University of Texas at Austin

Gaurav Sanwal

Chandigarh University

Arnav Patnaik

Chandigarh University

Shweta Chauhan

National Institute of Fashion Technology

Cooperative Institutions

Chandigarh University

Jaypee Institute of Information Technology

Indian Veterinary Research Institute

Graphic Era University

National Institute of Fashion Technology

Manav Rachna International Institute of Research and Studies

Jaypee University of Information Technology

Lovely Professional University

Galgotias University

University of Delhi

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Deepfake Detection System Using Deep Neural Networks

Madan Lal Saini Arnav Patnaik Mahadev Dayal Chandra Sati Ratish Kumar

The rapid progress in technology and automation has enabled sophisticated manipulation of multimedia content, blurring the line between real and fabricated media. Deepfake technology, driven by deep learning and Generative Adversarial Networks (GANs), creates hyper-realistic fake content, with applications spanning video games, films, and advertising. However, this technology also carries substantial societal risks, fostering misinformation and explicit content. To mitigate these concerns, this paper presents a Deepfake detection system that utilizes deep neural networks to discern genuine from forged images. Frames are extracted from videos and face detection and face cropped are performed. LSTM and ResNext CNN are utilized to generate a feature vector. The proposed system uses the Anvil platform to design the front end and Visual Studio and Jupyter Notebook for the back end. A publicly available dataset was used to train and test the model. The proposed model achieved an impressive 86% accuracy on video dataset.

Deep Neural Networks

10.1109/ic457434.2024.10486659

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FPGA implementation of a fuzzy logic based handoff controller for microcellular mobile networks

Dayal Chandra Sati Yogesh Misra

In mobile cellular systems the handoff is a very important process, which refers to a mechanism that transfers an ongoing call from one Base Station (BS) to another. The performance of the handover mechanism is very important to maintain the desired Quality of Service (QoS). The conventional handoff decisions are normally signal strength based, which are not suitable for modern small sized microcellular networks . In order to maintain reliable communication in a microcellular system new and better handoff algorithms must be needed to keep QoS as high as possible. Handoff algorithms, based on soft computing techniques such as Fuzzy Logic, Neural Networks etc can be used for the same purpose. The purpose of this work is to design and implement a Fuzzy Logic based Handoff controller on an FPGA. The tools used are: MATLAB 7.6.0 for designing FIS and Xilinx 9.2i for FPGA implementation.

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Fuzzy Logic Based Handoff Controller for Microcellular Mobile Networks

Dayal Chandra Sati P.Sirish Kumar Yogesh Misra

In mobile cellular systems the handoff is a very important process, which refers to a mechanism that transfers an ongoing call from one Base Station (BS) to another. The performance of the handover mechanism is very important to maintain the desired Quality of Service (QoS). The conventional handoff decisions are normally signal strength based, which are not suitable for modern small sized microcellular networks. In order to maintain reliable communication in microcellular mobile systems, new and better handoff algorithms must be needed to keep QoS, as high as possible. The purpose of this research work is to design an intelligent handoff controller, using fuzzy logic. Fuzzy logic toolbox of MATLAB 7.6.0 is used for designing FIS.

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CyberShield: A Hybrid CNN-GRU Model for Intelligent DDoS Attack Recognition

Shubrika Sharma himanshu .Dayal Chandra Sati Pardeep Kumar Monika Saini

10.1109/innocomp63224.2024.00069

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Handwritten English Script Recognition System Using CNN and LSTM

Madan Lal Saini Rahul Sai Telikicharla Mahadev Dayal Chandra Sati

10.1109/inc460750.2024.10649099

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Analytical Study on Humidity Sensing of Nanostructured Tin Oxide

Journal of Nanoscience Nanoengineering and Applications (2021)

Vivek Kumar Manoj Kumar Dayal Chandra Sati Parveen Kumar

Nanocrystalline tin oxide powder was prepared by a mechanochemical method. The synthesised powder was characterised using X-ray diffraction (XRD) and scanning electron microscopy (SEM). XRD shows the crystalline nature of the synthesised material. The crystallite size was estimated using Debye–Scherrer equation and its minimum value was 9 nm. Surface morphologies of the sensing pellets were investigated using SEM. Pellets as well as thick films were used as sensing elements for humidity sensing measurement. Thick film was prepared on alumina substrate using screen printing technique. Solid-state pellets as well as films were subjected to humidity-sensing measurements in a specially designed humidity chamber. Variations in resistance with relative humidity (%RH) were measured. The effects of annealing on the surface morphologies as well as on the sensitivity of the sensor were also investigated. Hysteresis and ageing effects on experimental results were found 60% and 64%, respectively, for the sensing element prepared after annealing at 600°C.

Nanocrystalline material

Tin oxide

Scherrer equation

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Simulation of Multirobot Movement Algorithms for Entity Detection

Dayal Chandra Sati Pardeep Kumar Monika Saini

The problem which is addressed in this paper is to find entities by multi-robot in an unknown environment. Here it is examined that how the choice of movement algorithm can affect the success of finding the entities in an unknown environment. Assumptions are that there is no central control, and robots have simple processing power and simple sensors and no active communication. Three different movement algorithms are evaluated which can gain good performance in the different unknown environment.

Movement control

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Micro-Sleep Accident Prevention by SMART Vehicle Using AI and Image Processing

Sonali Dubey Vansh Samadhia Sarthak Chauhan Nirmiti Deshmukh Dayal Chandra Sati

This research paper proposes a novel approach to prevent accidents caused by microsleep in smart vehicles through the integration of image processing and artificial intelligence. Microsleep is a brief period of unconsciousness that can occur during monotonous driving, leading to serious accidents. The proposed system aims to detect signs of microsleep in drivers and alert them to take corrective measures. The system uses a camera to capture images of the driver's face, which are analysed using image processing techniques to detect signs of fatigue or drowsiness. Apart from this we have come up with a smart steering wheel proposition, that monitors the data from pressure sensor installed in the wheel and launches an alert when the value drops or deviates from the normal threshold. An artificial intelligence model is then used to classify the level of fatigue and trigger appropriate alerts, such as auditory warnings, to the driver. The proposed system was tested on a dataset of real-world driving scenarios, and the results showed promising accuracy in detecting microsleep and preventing accidents. This system could potentially improve road safety and reduce the risk of accidents caused by driver fatigue in smart vehicles.

Unconsciousness

10.1109/asiancon58793.2023.10270583

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Hierarchical Approach for Multi-Class Models

Gaurav Sanwal Akshita Sharma Satyam Kumar Shweta Chauhan Dayal Chandra Sati

10.1109/icic3s61846.2024.10603345

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Application of Soft Computing Techniques for Handoff Management in Wireless Cellular Networks

Dayal Chandra Sati

Handoff process enables a cellular system to provide continuation of an active call when user moves from one cell to another. The modern cellular industry is using smaller cell sizes in order to increase the system capacity by using frequency reuse. Conventional handoff decisions are normally signal strength based. To make a better handoff and keep Quality of service (QoS) in wireless networks several handoff algorithms, based on soft computing techniques can be used. This paper highlights the basic handoff mechanism and a brief description about some of the soft-computing techniques which can be applied for handoff management in modern cellular networks. At last I have proposed a Fuzzy Logic based handoff technique using Fuzzy tool of MATLAB 7.6.0.

Soft handover

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