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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sharma, Amit Swami, Narendra Kumar Mittal, Rohit Hiran, Kamal Kant Arya College of Engineering & It Electronics & Communication Engineering Rajasthan Jaipur India Manipal University Jaipur Department of Computer & Communication Engineering Rajasthan Jaipur India Department of Computer Science & Engineering India

ISBN: (纸本)9798350384277

Rapid population growth throughout the world has resulted in the improper management of trash in many nations, which has led to a number of health problems and environmental damage. Only once or twice a week are collected by the e-waste collection vehicles. The garbage in the dustbin gets dispersed across the streets as a result of poor waste collection procedures. Suggested solutions are based on IotT and ML in this study to address the problem of smart and effective trash management. The authors' suggested approach makes use of an microcontroller and sensors. The e-waste index of a certain disposal site can be calculated using image processing. For the suggested framework, a hardware prototype is also created. The goal of creating clean, pollution-free cities is thus achieved by the suggested approach for effective e-waste management. © 2024 IEEE.

关键词： Waste management

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Autoencoder-Based Architecture for Identification and Mitigating Phishing URL Attack in IoT Using DNN

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Journal of The Institution of Engineers (India): Series B 2023年第6期104卷 1227-1240页

作者： Gopal, S.B. Poongodi, C. Nanthiya, D. Kirubakaran, T. Kulavishnusaravanan, B. Logeshwar, D. Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal India Department of Computer Technology-UG Kongu Engineering College Perundurai India

As a new and potentially devastating form of cyberattack, ‘Phishing’ URLs pose a risk to users by impersonating legitimate websites in an effort to obtain sensitive information such as usernames, passwords, and financial details. At least two-thirds of persons were hit by these phishing assaults in 2016, according to a survey (Gopal et al. in IOP Conference Series: Materials science and engineering, vol. 1055, no. 1, p. 012072. IOP Publishing). Therefore, these attacks must be addressed carefully to prevent financial losses. In order to detect and prevent new kinds of assault, in addition to the ones already in use, a defense mechanism that can learn the possibilities of attacks is required. In the IoT (Internet of Things), everything is managed remotely via a web address (URL). So, intruders can simply gain access to the controls by stealing the URL used to access them from a distant location. To identify and prevent access to phishing sites at the network layer, we offer an approach that combines a deep neural networks (DNN) model with autoencoders. In order to train the model for feature reduction, an autoencoder sends it a dataset that includes both legitimate and phishing domains to analyze. The effectiveness of the suggested approach has been tested on a number of datasets, including Open phish, UCI, Mendeley, and PhishTank. For the multiclassification dataset, these metrics for this design are: 92.89% accuracy, 93.07% recall, 92.75% precision, and 92.21% F1-score. © 2023, The Institution of Engineers (India).

关键词： Deep neural networks

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Haemoglobin Measurement from Eye Anterior Ciliary Arteries through Borescope Camera

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computer Systems science & engineering 2023年第2期44卷 1763-1774页

作者： Mohamed Abbas Ahamed Farook S.Rukmanidevi N.R.Shanker Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering CollegeChennai600062India Department of Electronics and Communication Engineering R.M.D Engineering CollegeChennai601206India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai600055India

Nowadays,smartphones are used as self-health monitoring devices for ***-health monitoring devices help clinicians with big data for accurate diagnosis and guidance for treatment through repetitive *** measurement of haemoglobin requires for pregnant women,pediatric,pulmonary hypertension and obstetric *** haemoglobin measurement through conjunctiva leads to inaccurate *** inaccuracy is due to a decrease in the density of goblet cells and acinar units in Meibomian glands in the human eye as age ***,conjunctivitis is a disease in the eye due to inﬂammation or infection at the *** is in the form of lines in the eyelid and covers the white part of the ***,small blood vessels in eye regions of conjunctiva inﬂammations are not visible to the human eye or standard *** paper proposes smartphone-based hae-moglobin(SBH)measurement through a borescope camera from anterior ciliary arteries of the eye for the above *** proposed SBH method acquires images from the anterior ciliary arteries region of the eye through a smartphone attached with a high megapixel borescope *** anterior ciliary arteries are projected through transverse dyadic wavelet transform(TDyWT)and applied with delta segmentation to obtain blood cells from the ciliary arteries of the ***,the Gaussian regression algorithm measures haemoglobin(Hb)with more accuracy based on the person,eye arteries,red pixel statistical parameters obtained from the left and right eye,age,and ***,the experimen-tal result of the proposed SBH method has an accuracy of 96%in haemoglobin measurement.

关键词： Hemoglobin measurement borescope camera smartphone anterior ciliary arteries region

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Privacy-preserving cross-domain recommendation using hybrid federated transfer learning

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Multimedia Tools and Applications 2025年第18期84卷 19343-19377页

作者： Goyal, Samta Jain Goyal, Rajeev Singh, Vinay Kumar Arunachalam, Rajesh Tripathi, Kuldeep Narayan Computer Science and Engineering ASET Amity University Madhya Pradesh Madhya Pradesh Gwalior India Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Thandalam Chennai India Madhya Pradesh Gwalior India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

Cross-domain recommender systems, which serve as a panacea to data sparsity and cold-start problems, are increasingly vital for providing personalized content across diverse application domains like medicine, e-commerce, etc. This is accomplished by leveraging adequate ratings ad user profiles in one domain to ensure precise recommendations in another domain. However, the domains with good ratings are unwilling to share user ratings with third-party recommender systems/domains due to privacy-concern. It demands the need for privacy mechanism to transfer knowledge between domains securely. So, this research introduces the CrossRec-Hybrid Federated Transfer Learning (CrossRec-HFTL) framework to address cold start and privacy problems in a cross-domain recommendation system, which combines adversarial domain adaptation and federated learning to leverage user and item interactions from a labeled source domain to generate recommendations in an unlabeled target domain. CrossRec-HFTL uses a model architecture called CrossRec-CDAN, which consists of multiple generators and discriminators for domain adaptation. The generators produce user, item, and interaction embeddings, while the discriminators distinguish between source and target domain samples. Federated learning enables collaborative model training across multiple clients while maintaining data locality, further bolstered by encryption techniques for privacy preservation. The framework was evaluated on the Amazon dataset across three domain pairs: Home and kitchen to Office Products (HK → OP), CDs and vinyl to Digital Music (CDs → DM) and Digital Music to Music Instruments (DM → MI). The simulation findings reveal that CrossRec-HFTL outperforms baseline and modern recommendation models in terms of recommendation accuracy, achieving a very less RMSE of 0.82 and MAE of 0.58. This shows the robust performance of CrossRec-HFTL in cold-start scenarios and enhanced privacy preservation. © The Author(s), under exclusive lice

关键词： Recommender systems

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A deep learning-based approach for detection of lung cancer using self adaptive sea lion optimization algorithm (SA-SLnO)

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12933-12947页

作者： Pradhan, Kanchan Chawla, Priyanka Rawat, Sanyog School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering Manipal University Jaipur India

The main intent of this paper is to design and implement a novel methodology for lung cancer prediction using the patient's health record. Feature extraction is performed using two well-performing approaches like principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). As an innovative contribution, a novel optimized correlation-based weighted feature extraction method named as self adaptive sea lion optimization algorithm (SA-SLnO) has been proposed that uses the latest meta-heuristic algorithm to optimize the weights. The number of hidden neurons in RNN is optimized by the proposed SA-SLnO. The major objective of this proposed lung cancer diagnosis model is to minimize the MSE among the actual and predicted outputs. The experimental findings demonstrate that the proposed model outperforms existing methodologies in terms of accuracy, sensitivity, and precision, FPR, FNR, and F1-score. Experimental finding demonstrates that proposed work outperforms PSO-RNN, GWO-RNN, GSO-RNN, and SLnO-RNN in terms of accuracy by 1.7 percent, 2%, 5%, and 2.7%, respectively. Similarly, the proposed lung cancer diagnosis model performed better when all performance metrics are examined on two datasets. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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Cyber intrusion detection using dual interactive Wasserstein generative adversarial network with war strategy optimization in wireless sensor networks

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Multimedia Tools and Applications 2025年第18期84卷 19223-19253页

作者： Anusha, N. Tapas Bapu, B.R. S, Selvakumaran Vijayaraj, A. Ramesh Kumar, C. P, Raji Department of IoT School of Computer science and Engineering VIT Tamil Nadu Vellore India Department of Electronics and Communication Engineering S.A.Engineering College Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Information Technology R.M.K Engineering College R.S.M Nagar Tamil Nadu Kavaraipettai India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India

Wireless sensor network (WSN) is one of the essential components of a multi-hop cyber-physical system comprising many fixed or moving sensors. There are many common attacks in WSN, which can quickly harm a WSN system. In this manuscript, Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) with war strategy optimization (WSO) algorithm is introduced for detecting intrusion in WSN. Initially, the input data is attained from the NSL-KDD and CICIDS2017 datasets. The data is then passed onto Morphological filtering and Extended Empirical wavelet transformation (MFEEWT) based pre-processing. After that, the filtered data is sent to the DIWGAN, which detects the intrusion and classifies the fraud data in the network. Additionally, the WSO algorithm is introduced to enhance the classification parameters of DIWGAN. By then the performance of the proposed DIWGAN-WSO-IDS methodology is evaluated using Python Platform. Also, the performance of the proposed methodology is calculated using various metrics like accuracy, precision, recall, f-measure, mean square error, and attack detection rate. Thus, the proposed technique has attained 29.87%, 31.7%, and 26.8% higher accuracy, 23.54%, 20.09%, and 26.98% higher recall, 12.76%, 10.96%, and 6.7% higher precision. And 6.85%, 4.56%, and 8.56% lower MSE than the existing techniques like BFSGSRF, MHICA-SSA, and CNN-LSTM methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wireless sensor networks

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Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8587-8603页

作者： Sakthimohan, M. Deny, J. Elizabeth Rani, G. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Department of Computer Science and Engineering Kalaignar Karunanidhi Institute of Technology Tamil Nadu Coimbatore India

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The

关键词： Intrusion detection

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Federated Multitask Learning for Complaint Identification Using Graph Attention Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1277-1286页

作者： Singh, Apoorva Chandrasekar, Siddarth Sen, Tanmay Saha, Sriparna Department of Computer Science and Engineering Indian Institute of Technology Patna Patna801106 India Department of Electronics and Communication Engineering Iiitdm Kancheepuram600127 India Ericsson Kolkata700135 India

Prior study on automatically identifying complaints on social media has relied on extensive feature engineering in centralized settings, with no consideration for the decentralized, nonidentically independently distributed (Non-IID), and privacyconscious aspect of complaints, which can hinder data collection, distribution, and learning. In this work, we propose a graph attention network (GAT)-based multitask framework that intends to learn two closely related tasks, complaint detection (primary task) and sentiment classification (auxiliary task), simultaneously in federated learning scenarios. We propose the Federated Combination (FedComb) algorithm, a two-sided adaptive optimization technique that simultaneously optimizes global and local models. The proposed methodology outperforms several baselines for the intended task of recognizing complaints in decentralized settings, according to quantitative and qualitative studies on two benchmark datasets. Impact Statement-Automatic complaint identification is crucial to organizations as they provide insights to meet the customers' requirements, including handling and addressing the complaints. Unlike standard technologies developed using data stored in the cloud, a federated learning model allows businesses to learn complaint detection models without sharing their training data spread on their local databases with third-party cloud services. In this work, we propose a GAT-based sentiment-aided complaint identification in federated learning settings. The proposed FedComb algorithm integrates local and global model optimization, making it efficient compared to existing federated algorithms. © 2022 IEEE.

关键词： Deep learning

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Efficient resource allocation for D2D-enabled social IoT networks:A tripartite and time-scale optimization approach

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Intelligent and Converged Networks 2024年第4期5卷 380-401页

作者： Saurabh Chandra Rajeev Arya Maheshwari Prasad Singh Department of Electronics&Communication Engineering National Institute of Technology PatnaPatna 800005India Department of Computer Science and Engineering National Institute of Technology PatnaPatna 800005India

In the densification of Device-to-Device(D2D)-enabled Social Internet of Things(SIoT)networks,improper allocation of resources can lead to high interference,increased signaling overhead,latency,and disruption of Channel State Information(CSI).In this paper,we formulate the problem of sum throughput maximization as a Mixed Integer Non-Linear Programming(MINLP)*** problem is solved in two stages:a tripartite graph-based resource allocation stage and a time-scale optimization *** proposed approach prioritizes maintaining Quality of Service(QoS)and resource allocation to minimize power consumption while maximizing sum *** results demonstrate the superiority of the proposed algorithm over standard benchmark *** of the proposed algorithm using performance parameters such as sum throughput shows improvements ranging from 17%to 93%.Additionally,the average time to deliver resources to CSI users is minimized by 60.83%through optimal power *** approach ensures QoS requirements are met,reduces system signaling overhead,and significantly increases D2D sum throughput compared to the state-of-the-art *** proposed methodology may be well-suited to address the challenges SIoT applications,such as home automation and higher education systems.

关键词： Device-to-Device(D2D)communication Social Internet of Things(SIoT) resource allocation Quality of Service(QoS) tripartite graph time scale

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EU-Net: Efficient U-shaped Deep Convolutional Neural Network for Colon Polyps Segmentation 6

EU-Net: Efficient U-shaped Deep Convolutional Neural Network...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Pratik, Shreerudra Sharma, Pallabi Rana, Debaraj Balabantaray, Bunil Kumar NIT Meghalaya Computer Science and Engineering India UPES Dehradun School of Computer Science India NIT Meghalaya Electronics and Communication Engineering India

ISBN: (纸本)9798350379297

Deep learning has emerged as a transformative approach in the realm of polyp segmentation within colonoscopy images. In the pursuit of advancing this segmentation approach, this study introduces a novel deep learning architecture that synergistically combines the concept of EfficientNet-B7 and U-Net and propose a new architecture named EU-Net. The EfficientNet-B7, recognized for its scalable and powerful feature extraction capabilities, serves as the encoder of EU-Net. In contrast, the U-Net, renowned for its capability in biomedical image segmentation, functions as the decoder. The prime contribution of the proposed model lies in the analysis of different attention mechanisms before the concatenation of skip connection and the upsampling feature. This attention mechanism enhances the model's focus, ensuring it retains critical regions within images, thereby boosting the accuracy of segmentation tasks. Furthermore, the EU-Net model is enriched with dilated convolutions, enabling it to capture multi-scale context without the loss of resolution, a paramount feature for detecting polyps of varying sizes. The proposed model was rigorously evaluated using the CVC-ClinicDB, a benchmark database for colonoscopy image analysis. The proposed model achieves an IoU of 0.8863. Preliminary results showcase a substantial improvement in segmentation performance, highlighting the potential of the proposed Eu-Net in advancing colorectal cancer prevention efforts. © 2024 IEEE.

关键词： Convolutional neural networks

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