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Baker’s Map Based Chaotic Image Encryption in Military Surveillance Systems 1

International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Rajendiran, Nithya Paranthaman Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the Visual sensor network and the remotely operated devices. There are several cryptographic algorithms available to secure the visual information. Several chaotic schemes worked well because they were more resistant to threats and had greater entropy levels. But, it takes a lot of time to finish the encryption step. Traditional approaches had the drawback that increasing encryption strength slowed calculation speed. This paper presents a scheme called "baker’s map chaotic method based image encryption" to accelerate the encryption process in a network of visual sensors without weakening the encryption method. The findings indicate that the system’s functionality is improved when the computation time is reduced to sixty seconds and the PSNR is increased to 15 dB. The performance of the chaotic method is compared by using key space analysis, key sensitivity analysis, correlation coefficient analysis, noise analysis, Entropy analysis, and Speed analysis. The IBM SPSS statistical tool was also used to do statistical analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sensitivity analysis

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Machine Learning Techniques for Effective Content- Based Approach in the Detection of Fake News 4

Machine Learning Techniques for Effective Content- Based App...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Jammula, Roshini Devi Srinivas, Nukala Bhargav Sandeep, K. Parimala, G. Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350360165

All functionalities required in the process of fake news detection, from data preparation to model building, have been done within the project, smoothly integrated into Flask for user interaction. The first step of data processing requires the import of the packages and the merging of real and fake news datasets. Data cleaning includes the removal of duplicates, text cleaning, and insightful visualization of characteristics using Seaborn and Matplotlib. Tokenization via TF-IDF Vectorization, Feature Selection, and Data Splitting paves the way for an extensive suite of Classification Models from Logistic Regression and SVC to KNN, Random Forest, Stacking, and even Voting Classifiers. Additionally, Flask provides functions for user registration, signing in, and an inbuilt SQLite database to store the user data. User input text is set to be translated and preprocessed for prediction using the best-fit pre-trained model. The outcome will then be shown to the user on the frontend. The novelty with the extension here is the models like the Voting Classifier (XGB + PA + Boosting) that have been introduced, which gives a 100% accuracy. More importantly, it extends the usability feature by allowing the user to provide text at the front end and get real-time predictions, thus enriching the overall user experience. In more ways than one, this project, from its approach toward fake news detection to its diversity of methodology and user-centered design, is going to be the most apropos project in the coming days to the emerging landscape of information integrity. © 2024 IEEE.

关键词： Inverse problems

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An Efficient AI Model for Classifier Tweets During Disaster Using Recurrent Neural Networks (RNN) 2nd

An Efficient AI Model for Classifier Tweets During Disaster ...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Lakshmi Narayanan, K. Naresh, R. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai603 203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai603 203 India

ISBN: (纸本)9789819754113

During any significant event, particularly during natural catastrophes, social media is an essential information source. It is claimed that data generated by social networking sites would enable regular people to become more situationally aware during disasters and coordinate to help themselves because it is ubiquitous, quick, and accessible. However, as the amount of social media data has grown exponentially, so has the amount of data that are unrelated to a crisis, making it harder for individuals to find the information they need to organise relief efforts, seek assistance, and perhaps even save lives. In this essay, we offer a method for identifying. During times of tragedy, informative messages are posted on social media. Our strategy is to implement this using recurrent neural networks and demonstrate substantial progress. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Real Time Face Emotion Detection with CNN 2nd

Real Time Face Emotion Detection with CNN

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Bharadwaj, Samhita Harshitha, N. Naresh, R. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031689048

A model that uses the Convolutional Neural Network deep learning algorithm to identify emotions on a person's face in real time is proposed in order to further the technology of recognition of facial expression used in machine learning and artificial intelligence (CNN). In order to find the face in the picture and register it in the face detection stage, image pre- processing is first carried out, in which a grayscale image is produced from the input image. During the extraction of face features, a feature vector is created once the facial features have been extracted. The facial expression that is recognized during the emotion classification stage is categorized into one of a human's seven primary emotions. With the use of CNN, the proposed model has been observed to provide great accuracy and improved performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

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International Journal of computers and Applications 2024年第5期46卷 301-309页

作者： Ranjan, Rajeev Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna India Department of Computer Science and Engineering Bakhtiyarpur College of Engineering Bakhtiyarpur India

Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Motion planning

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Smart Cloud Monitoring: Optimising Server Loads for Reducing Energy Consumption 8

Smart Cloud Monitoring: Optimising Server Loads for Reducing...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Vignesh, R. Thenmalar, S. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798350367904

The recent proliferation of cloud computing services brings forth the focal issues of energy consumption in data centers from an environmental and financial standpoint. This paper describes a monitoring system focused on energy consumption reduction in the cloud by dynamically regulating loads in servers through constrained optimization techniques. The system employs the Gated Recurrent Unit algorithm in order to analyze the patterns of server usage over time and predict future demand, thereby allowing for proactive adjustments in workload distribution. The system maintains an optimal balance between performance and energy efficiency by continuously monitoring server utilization and power consumption, taking into consideration others like actual computation abilities, temperature limits, and SLAs. Our approach intensely reduces the power consumption while keeping a similar service quality level. Our testing results have proven to work well in real-world cloud environments, thus forming a basis that will help enhance sustainable practices in the cloud. © 2024 IEEE.

关键词： Cloud Computing Constrained Optimization Data Centers Energy Consumption Green Computing Load Balancing Server Load Optimization

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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2025年第17期84卷 17389-17433页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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Alzheimer's Disease Prediction using VGG16 and Transfer Learning 3

Alzheimer's Disease Prediction using VGG16 and Transfer Lear...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Uprit, Maharth Monish Khattar, Pranav Brahmbhatt, Malhar Preethiya, T. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9798331527549

Alzheimer’s Disease (AD) prediction is essential for early detection and effective treatment. In this paper, a hybrid deep learning model has been proposed for predicting Alzheimer’s Disease in a patient in their early years. The dataset consists of Magnetic Resonance Imaging (MRI) scans that have four classes namely, non-demented, very mild demented, mildly demented, and moderately demented. In this hybrid model, fine-tuned VGG16 and transfer learning models were used with hyperparameter tuning to adapt it to Alzheimer's Disease prediction. The simulation results show that the proposed hybrid model outperforms the existing VGG16 model with an accuracy of 97.23% © 2025 IEEE.

关键词： Prediction models

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A review on spreading and Forensics Analysis of Windows-Based ransomware

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Annals of Data science 2024年第5期11卷 1503-1524页

作者： Chayal, Narendrakumar Mangilal Saxena, Ankur Khan, Rijwan Department of Computer Science and Technology Faculty of Engineering and Technology Department of Computer Engineering Sankalchand Patel University Visnagar India Department of Computer Science Indus Institute of Information and Communication Technology Dean-ICT & ampDirector-IIICT Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad Affiliated to AKTU Lucknow India

Ransomware is one of the most advanced malware which uses high computer resources and services to encrypt system data once it infects a system and causes large financial data losses to the organization and individuals. There are certain automatic ransomware detection and analysis strategies available nowadays. File system analysis reveals some essential patterns and artifacts that can be very useful to understand its behavior spreading mechanism, taxonomy for malware forensics experts. Current trend explores Ransomware as a service (RaaS) and Malware as a service (MaaS) on Darknet. This paper reveals a theory of digital forensic methodology to identify the spreading/infection mechanism and attack path, the cryptographic methodology, windows services, process, APIs, persistence mechanism, and system lockdown strategies and malware analysis methodology. This review could be helpful to learn and understand malware forensic analysis for threat researchers, students, cyber experts, etc. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Malware

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Introduction to the Special Issue on the Bottleneck of Blockchain Techniques Scalability,Security and Privacy Protection

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computer Modeling in Engineering & sciences 2024年第12期141卷 1933-1937页

作者： Shen Su Daojing He Neeraj Kumar Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004India

Blockchain technology has been extensively studied over the past decade as a foundation for decentralized information-sharing platforms due to its promising *** the success of existing blockchain architectures like Bitcoin,Ethereum,Filecoin,Hyperledger Fabric,BCOS,and BCS,current blockchain applications are still quite *** struggles with scenarios requiring high-speed transactions(e.g.,online markets)or large data storage(e.g.,video services)due to consensus efficiency *** restrictions pose risks to investors in blockchain-based economic systems(e.g.,DeFi),deterring current and potential *** protection challenges make it difficult to involve sensitive data in blockchain applications.

关键词： services Ether limitations

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