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A threefold chaotic image encryption technique for efficacious image data protection

International Journal of computers and Applications 2024年第11期46卷 974-982页

作者： Anwar, Shamama Srivastava, Anshuman Department of Computer Science and Engineering Birla Institute of Technology Ranchi India

With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. This paper provides a summary of the techniques of securing image data focusing primarily on encryption. This paper additionally also explores the contribution of chaos theory in the domain of image encryption and applies a threefold chaotic map sequence to design an image encryption technique. This chaos-based encryption technique renders characteristics like sensitivity, determinacy, and ergodicity, and hence provides a better trade-off between security and computational speed. The efficacy of proposed algorithm is also evaluated based on well-known statistical metrics like Number of Changing Pixel Rate (NPCR), Unified Average Changing Intensity (UACI), Information Entropy, local Entropy, Correlation analysis, key sensitivity analysis, and NIST Randomness test. Furthermore, the key sensitivity analysis is also discussed which is a major part of any encryption algorithm analysis. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensitivity analysis

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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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A BERT-encoded ensembled CNN model for suicide risk identification in social media posts

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Neural Computing and Applications 2024年第18期36卷 10955-10970页

作者： Gorai, Joy Shaw, Dilip Kumar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Suicide is a significant public health issue that devastates individuals and society. Early warning systems are crucial in preventing suicide. The purpose of this research is to create a deep learning model to identify suicide risk using a combination of bidirectional encoder representations from transformers (BERT) and an ensemble of multiple convolutional neural networks (CNN). BERT is used to encode the text data into numerical representations that capture the context-aware meaning of words and phrases, while ensemble CNN is used to analyze the encoded text data to identify patterns and relationships relevant to suicide risk. The model is trained on a large corpus of text data from social media and suicide notes and evaluated on a validation set of labeled data. The results of the proposed model perform better than the recent approaches in detecting suicide risk and can potentially be a valuable tool for suicide prevention efforts. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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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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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Revolutionizing Bus Tracking and Arrival Prediction in Real Time for Both Public and Private Sectors, Powered by Firebase 3

Revolutionizing Bus Tracking and Arrival Prediction in Real ...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shafiulla, Mohammed Abdul Lateef Haroon, P.S. Naveed, Shaik Mohammed Ahmed, Suhail Mahika Dhar, M. Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology And Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India

ISBN: (纸本)9798350318609

Public transport systems often face challenges related to reliability and efficiency, leading to inconvenience and uncertainty for passengers. Existing solutions for real-time bus monitoring typically rely on expensive Internet of Things (IoT) components. This paper presents Track N Go, a cost-effective alternative for real-time monitoring of public buses and taxis using mobile phone location data. The system utilizes a robust backend system built with Java and *** for the user interface. Integration of GPS via the Map Box API and Firebase database ensures accurate location tracking. Track N Go provides accurate real-time monitoring, addressing challenges associated with public transport and improving overall urban mobility. The system enhances convenience and peace of mind for passengers by delivering reliable and user-centric solutions. The evaluation of Track N Go demonstrates its effectiveness in improving urban mobility and transit services. By offering a reliable and cost-effective solution for real-time bus monitoring, Track N Go aims to streamline daily commutes and make public transport more efficient and predictable. © 2024 IEEE.

关键词： Urban transportation

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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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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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Intrusion detection system and fuzzy ant colony optimization based secured routing in wireless sensor networks

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Soft Computing 2024年第17-18期28卷 10345-10367页

作者： Subramani, Shalini Selvi, M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Recent advances in wireless sensor networks (WSNs) have brought the sensor based monitoring developments to the surface in many applications. In such a scenario, the security of communication is a major challenge in the effective delivery of the collected data due to the presence of malicious nodes. Moreover, since security and minimization of energy consumption are critical factors in designing techniques for multi-hop secure routing in wireless sensor networks, it is necessary to address the issues of security in the routing process. Hence, this paper proposes a novel intrusion detection system for enhancing the security and Fuzzy based Ant Colony Optimization based Secured Quality of Service Routing Protocol (F-ACO-SQoSRP) for increasing the security of communication and network performance in WSNs. Using this proposed intrusion detection system (IDS), the proposed model identifies the distinct and malicious behaviours of nodes. Additionally, a clustering algorithm has been proposed in this work, wherein the cluster head selection (CHS) is based on quality of service (QoS) measures and the trust values of nodes are measured using the intrusion detection system results. The proposed ACO based routing framework also predicts the best optimum and secured path to allow for effective communication across each link. The simulation results obtained from this work proved that the proposed secured routing algorithm provides better performance in terms of security using robust trust values, increase in packet delivery ratio and network lifetime reduction in delay and energy consumption when this work is compared with the existing secured routing systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Quality of service

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Advanced deep learning models for automatic detection of driver’s facial expressions, movements, and alertness in varied lighting conditions: a comparative analysis

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Multimedia Tools and Applications 2025年第6期84卷 2947-2983页

作者： Das, Shiplu Pratihar, Sanjoy Pradhan, Buddhadeb Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani India University of Engineering and Management Kolkata West Bengal New Town India

Emotion recognition is vital in understanding human behavior and improving the driving experience. We present a systematic analysis of automatic detection of driver’s facial expressions and movements while crying, feeling sad, experiencing anger, drowsiness, fear and surprise to reduce road accident using a deep learning method in varied lighting conditions. The proposed approach aims to accurately detect and analyze driver emotions in real time, it would help in advancing the growth of smart driver assistance systems. To conduct our analysis, we create a comprehensive dataset comprising annotated facial images of drivers exhibiting crying, sadness, anger, drowsiness, fear and surprise. The dataset is carefully designed to encompass diverse lighting conditions, facial orientations, and driver demographics, ensuring the generalizability and robustness of the proposed method. The CNN-BiLSTM model is compared against the CNN, LSTM, CNN-LSTM, BiLSTM, and CNN-LSTM models in the research to perform precise localization of facial expressions and movements. The trial findings show how well our technique accurately identifies and evaluates a driver’s facial expressions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Advanced driver assistance systems

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