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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Chauhan, Sandeep Nene, Manisha J. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

The ad-hoc deployments of Wireless Sensor Network (WSN) have emerged as major technology which can enhance operational capability of critical surveillance operations at hostile as well as places where accessibility of human intervention is restricted however the main concern for their utilization in these areas is power consumption by WSNs model. To improve the power utilization many protocols are introduced such as Low Efficiency Adaptive Clustering Hierarchy (LEACH), Power efficient gathering in Sensor information System (PEGASIS), Threshold Sensitive energy Efficient Sensor (TEEN) etc. LEACH introduced the concept of clustering which was a part of Hierarchal protocols and works on Cluster Head concept. These protocols are generally classified in three parts flat, direct and hierarchal. Among all the other protocols LEACH and PEGASIS has shown improved results as compared to other. PEGASIS works on chain-based flow of information and works on leader head (LH), LH is chosen determining the closest distance of a Node from Sink station. PEGASIS gives a better efficiency in terms of transmission as compared to LEACH. This paper aims to compare two protocols PEGASIS and LEACH (Low Energy Adaptive Clustering Hierarchy) in a Dynamic WSN network with the help of algorithm. The result will help in building a better energy efficient protocol for WSN model. These protocols can be clubbed with other route optimizing techniques such as Ant Colony optimizer, Ant Bee Colony and Grey Wolf optimizer to improve their efficiency of the model therefore increasing the effectiveness of the model. Simulation is done in MATLAB. © 2022 IEEE.

关键词： Ant colony optimization

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Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) Algorithm

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SN computer science 2024年第1期5卷 1-18页

作者： Rollin, Ndom Francis Giquel, Sassa Chantal, Mveh-Abia Raoul, Ayissi Remy, Etoua Yves, Emvudu Department of Mathematics and Physical Sciences National Advanced School of Engineering of the University of Yaounde I Melen Yaoundé 8390 Cameroon Department of Computer Science Faculty of Sciences of the University of Yaounde I 812 Ngoa-Ekelle Yaoundé Cameroon Department of Computer Engineering National Advanced School of Engineering of the University of Yaounde I Melen Yaoundé 8390 Cameroon Department of Mathematics Faculty of Sciences of the University of Yaounde I Ngoa-Ekelle Yaoundé 812 Cameroon

Radial Basis Gated Unit-Recurrent Neural Network (RBGU-RNN) algorithm is a new architecture-based on recurrent neural network which combines a Radial Basis Gated Unit within the Long Short Term Memory (LSTM) network architecture. This unit then gives an advantage to RBGU-RNN over the existing LSTM network. Firstly, given that the RBGU is just an activation unit and which do not perform any weighted operations as it should in a classical neuron unit, it has an advantage for not propagating (duplicating) error as compared to the LSTM. Secondly, due to the fact that this unit is located at the beginning of the network treatment workflow, it provides standardization to the data set, before they are run into the weighted units, which is not the case of a simple LSTM. This study then provided a theoretical and experimental comparison of the LSTM and RBGU-RNN. Indeed, using a real world call data record, precisely a survey on the end user cell network data traffic, we built up a cellular traffic prediction model. We start with ARIMA model which permit us to choose the number of time steps needed to build the RBGU-RNN prediction model that is the number of time steps needed to predict the next individual in the time series. The results show that RBGU-RNN accurately predict cellular data traffic with great success in generalization than LSTM. The R-squared statistics or determination coefficients show that 58.31 % of user traffic consumption can be explained by LSTM model, while 96.86 % of the user traffic consumption can be done by RBGU-RNN model in the training set. Likewise, in the test set, we found that 61.24 % of user traffic consumption can also be explained by LSTM model and 95.20 % can be done by RBGU-RNN. Also, the RBGU-RNN has more efficient gradient descent than the standard LSTM by analysing and experimenting the graphs given by the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the Maximum Absolute Error (MAXAE) functions over the numbe

关键词： Feed forward algorithm Gated recurrent unit Long short term memory Neural networks Recurrent neural networks

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Forensic Analysis of Windows 11 Prefetch Artifact 4

Forensic Analysis of Windows 11 Prefetch Artifact

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Budhrani, Akash Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665492911

The Operating System creates numerous objects to improve its efficiency and user experience and such objects are called artifacts. These artifacts record crucial data about the user activity. Such artifacts are the start point of any investigation as they can be an additional lead to a forensic triage. Prefetch file is one among various objects, presence of which confirms the execution of a particular application. Prefetch gives additional inside for the purpose of investigation. Thus, this paper brings out the forensic value of it, the tools required to decode the information it contains and also look in various caveats in interpreting this artifact to learn its strength and weaknesses to properly incorporate in support of opinion derived by the analyst. In this work, Prefetch is forensically examined to bring out its forensic value, knowledge it contains and all of that in whole or in parts can be used to help advance in investigation. Paper also brings out the difference in format of this artifact among various version of Windows OS. © 2022 IEEE.

关键词： Windows operating system

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Detection of Multiclass Objects in Satellite Images Using an Improved Algorithmic Approach 4

Detection of Multiclass Objects in Satellite Images Using an...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Singh, Abhimanyu Nene, Manisha J Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665492911

Object Detection (OD) in natural images has made tremendous strides during the last ten years. However, the outcomes are infrequently adequate when the natural image OD approach is used straight to Satellite Images (SI). This results from the intrinsic differences in object scale and orientation introduced by the omniscient viewpoint of the SI. Detecting objects is a challenging task especially when small object areas and complicated backgrounds appear in satellite images under analysis. Occlusion and intense item overlap have a further negative effect on the detection performance. The self-attention mechanisms are proposed to search for minute details in an image. However such searches mechanism come with complexity or high computational cost due to uncertainty induced in visual resolutions. The study in this research paper addresses the problems experienced in the accuracy and precision and the efficacy of the proposed model is demonstrated with the result in this paper. © 2022 IEEE.

关键词： Object detection

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Real-Time Eyes Monitoring System Using OpenCV Autonomous filtering using RNN 3

Real-Time Eyes Monitoring System Using OpenCV Autonomous fil...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Prajapati, Dhruvansh Joshi, Jaynil Das, Nibedita Satapathy, Santosh Pandit Deendayal Energy University Information & Communication Technology Gujarat Gandhinagar India Institute of Advanced Research Department of Computer Science and Engineering Gujarat Gandhinagar India

ISBN: (纸本)9798350354171

This paper introduces a novel Real-Time Eye Gazing and Monitoring System aimed at objectively and continuously assessing individuals across various environmental conditions. Addressing the challenge of accurate performance in diverse lighting conditions, the system employs Recurrent Neural Network (RNN) trained filters to enhance eye detection accuracy. Traditional methods often falter in non-ideal lighting, prompting the need for adaptive solutions. Through extensive experimentation, optimal filter combinations were identified, ensuring robustness across a spectrum of scenarios. Implementation via an OpenCV facilitates efficient processing without compromising detection accuracy. This paper achieved the list of main filters and their combinations which can be helpful to detect eyes in different environmental conditions. © 2024 IEEE.

关键词： Recurrent neural networks

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Drone/UAVs Surveillance feeds Video Object Recognition Using Transfer Learning 1

Drone/UAVs Surveillance feeds Video Object Recognition Using...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Kumar, Sushil Kumar, C.R.S. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

The objective of this research is to demonstrate the use of a convolutional neural network (CNN) for object detection (OD) on drone videos (CNN). The goal of the study is to determine how well the YOLO V3 (Y-V3) and feature extractor recognise objects in drone-shot video frames the *** to the study, application of several target identification algorithms to 'regular' images captured by 'normal' cameras has different efficiency impacts on the quantity, precision, and target performance consumption, as well as when applying the technique to image data collected by drones. Unmanned aerial vehicles (UAVs), a very active domain in this field, are crucial to the implementation of any robot's complete autonomy due to its enhanced capability of OD. We have conducted numerous experiments to address our functional issues to investigate the efficacy of the most sophisticated target detection algorithm in the image data acquired by UAV. In this study, the algorithm's performance metrics are tallied after being tested on 1099 photos. © 2022 IEEE.

关键词： Object detection

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep Learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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Aircraft Detection in Satellite Images 1

Aircraft Detection in Satellite Images

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Singh, Abhimanyu Nene, Manisha J. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

Object detection is one of the key areas for all the researchers in the field of computer science. The research is to find the types of objects in the image and provide their temporal and spatial characteristics. In the recent times there have been a lot of improvements in the field of satellite image detection with varying data sets being available and has left high impact on the performance of such analysis. A number of algorithms have evolved over a period of time in object detection and analysis namely different versions of YOLO, CNN, DETR etc. There is a need to deploy a study which enables the performance comparison of different versions of these algorithms on specific data set to understand their efficacy. The study in the paper contributes to understanding, evaluating and analyzing the performance characteristics of YOLO v7 algorithm with varying parameters. © 2022 IEEE.

关键词： Object detection

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Forensic Techniques to Detect Hidden Data in Alternate Data Streams in NTFS 4

Forensic Techniques to Detect Hidden Data in Alternate Data ...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665492911

Alternate Data Streams (ADS) have been a feature of the New Technology File System (NTFS) since its introduction in 1993. Alternate Data Streams (ADS) were introduced to address compatibility within the existing Operating Systems. Lately Hackers/Cyber Criminals have used Alternate Data Streams (ADS) as a means for launching Cyber- Attacks. Alternate Data Streams (ADS) allow data hiding, same being difficult to detect without adequate knowledge. In this paper we shall bring out the various Forensic techniques in which hidden data in Alternate Data Streams (ADS) can be detected. Finally, we compared the Forensic techniques to detect data hidden in Alternate Data Streams (ADS) in both Windows 10 and 11 Operating System. © 2022 IEEE.

关键词： Network security

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Protecting machine learning systems using blockchain: solutions, challenges and future prospects

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Multimedia Tools and Applications 2024年 1-28页

作者： Hajlaoui, Rejab Dhahri, Salah Mahfoudhi, Sami Moulahi, Tarek Alotibi, Gaseb Department of Computer and Informtion Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Laboratory of Advanced Multifunctional Materials and Technological Applications Faculty of Science and Technology of Sidi Bouzid University of Kairouan Sidi Bouzid Tunisia Department of Information and Technology Management Mediterranean School of Business South Mediterranean University Tunis Tunisia Department of Information Technology College of Computer Qassim University Buraidah Saudi Arabia Department of Computers and Information Technology Alwajah College University of Tabuk Tabuk Saudi Arabia

Machine learning-based systems have emerged as the primary means for achieving the highest levels of productivity and efficiency. They have become the most influential competitive factor for many technologies and business companies such as Cloud AI Companies (Google, Facebook…) and Health Care AI Companies (Tempus, Nanox …). However, privacy and security issues have become the biggest challenge facing all ML applications. These threats are known as Adversarial Machine Learning (AML). Their main goal is to maliciously manipulate training data and model parameters to obtain misleading results. To overcome these challenges, several research studies have proven that Blockchain, which relies on cryptographic technologies, constitutes a promising solution for securing ML applications. It provides high guarantees to make the system scalable, reliable and more secure. In this survey, we provide an overview of ML applications and the most hostile attacks they face. Then, we define the blockchain technique and its distinctive features. After that, we conduct a comprehensive and detailed survey of the latest and best blockchain-based research to address security issues in ML applications (integrity, confidentiality and availability). We analyze, compare and comment on all the proposed solutions. In the end, we discuss in detail the various challenges that hinder the adoption of blockchain technology and we extract the most important scientific trends that will serve as guidance and support for researchers in their future works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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