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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornimathi, K. Muralibhaskaran, V. Priya, L. Rajalakshmi Engineering College Department of Computer Science and Business Systems India Rajalakshmi Engineering College Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Information Technology India

ISBN: (纸本)9798350376425

The digitization and preservation of Tamil inscriptions are crucial for safeguarding the rich cultural heritage they represent. This study presents an in-depth evaluation of deep learning-based segmentation methods specifically designed for the complex and ancient Tamil script. Evaluate several cutting-edge deep learning models, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and attention-based mechanisms, using a dataset of Tamil inscriptions. The evaluation criteria encompass segmentation accuracy, computational efficiency, and the ability to handle the noise and degradation common in historical artifacts. Our findings indicate that traditional methods often falter when faced with the complexities of Tamil script, whereas advanced deep learning techniques markedly enhance segmentation quality. This improvement facilitates more precise transcription and analysis. The comparison emphasizes the benefits and challenges of each method, delivering important insights into their effectiveness for cultural heritage preservation and paving the way for future research in the digitization of ancient scripts. © 2024 IEEE.

关键词： Convolutional neural networks

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Bernstein polynomials and Bezier curves: a novel modeling approach to secure ECG data transmission

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1043-1053页

作者： Rajagopal, Prahalad Premnath, Pooja Arumugam, Chamundeswari Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

In the digital era, protecting sensitive medical data, including Electrocardiogram (ECG) signals, is of utmost importance. Maintaining the confidentiality, integrity, and authenticity of ECG signals while safeguarding their privacy is a critical challenge. This paper proposes a novel approach to safeguard ECG waveforms by utilizing polynomial modeling techniques followed by encryption. Polynomial functions, specifically Bernstein and Bezier-Bernstein polynomials, are employed for accurate approximation of the ECG signal, accounting for noise, interference, and channel distortions. The coefficients obtained from polynomial modeling are encrypted using the standard AES and Fernet algorithms. Decryption allows authorized access to the ECG signals, facilitating methodical waveform reconstruction. The proposed methodology’s performance is evaluated using various metrics to assess its effectiveness in protecting sensitive medical data. Metrics include Root Mean Square Error, Peak Signal to Noise Ratio, Correlation Coefficient, and Structural Similarity Index. The proposed method effectively safeguards ECG waveforms by combining polynomial modeling and encryption techniques. The approach offers a robust solution to protect sensitive medical data in the digital realm. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bernstein polynomials Bezier-Bernstein polynomials Electrocardiogram (ECG) Signal decryption Signal encryption

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FOD-YOLO NET: Fasteners fault and object detection in railway tracks using deep yolo network1

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8123-8137页

作者： Brintha, K. Jawhar, S. Joseph Department of Computer Science and Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India Department of Electrical and Electronics Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. © 2024 – IOS Press. All rights reserved.

关键词： Object detection

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Advanced Remote Sensing Techniques for Underwater Fin Whale Detection in the Indian Ocean 16

Advanced Remote Sensing Techniques for Underwater Fin Whale ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sanikommu, Vasavi Sura, Akshaya Chimirala, Pranavi Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Andhra Pradesh India Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331505264

Accurate detection and tracking of cetaceans in the Indian Ocean remain some of the salient issues for marine conservation despite the abundant challenges that accompany the environment underwater. Such realistic avenues can go a long way in bridging the said challenges with advanced remote sensing technologies. In this study, hyperspectral imaging, satellite imagery, and thermal imaging combine to form an integrated system in whale detection. The hyperspectral imaging of whale skin provides spectral reflectance characteristics for detection under different oceanic conditions. Along with these, satellite imaging helps determine subsurface features while thermal imaging determines the thermal characteristics of whales. Machine learning algorithms analyze the data set amalgamation to enhance detection accuracy as well as reliability. The overall system effectively detected whales at the various depths and environmental conditions associated with the Indian Ocean. Hyperspectral imaging was sensitive to the turbidity levels of water and target reflectance, while the coastal blue band showed subsurface features of a whale. Thermal imaging also increased detection abilities in dim light as well. This three-in-one platform approached better than other methods with an accuracy of 0.96 and reliability by real-time whale monitoring. © 2024 IEEE.

关键词： Hyperspectral imaging

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis 1

Panoptic Region Slicing Segmentation and Optimized Alexnet-B...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pareddy, Sudha V. Virupakshappa PDA College of Engineering Department of Computer Science & Engineering Kalaburagi India Sharnbasva University Department of Computer Science & Engineering Kalaburagi India

ISBN: (纸本)9798350371802

The early and accurate diagnosis of melanoma, a potentially harmful skin cancer, is essential in enhancing the survival of patients. This paper describes a new method for detecting melanoma called Panoptic Region Slicing Segmentation (PRS2), which employs an AlexNet-based optimized convolution neural network (PRS2-OCNN). The proposed system integrates several advanced techniques to improve the efficiency and accuracy of melanoma detection. The first step is to preprocess the dermoscopic image with a 2D Fusion Filter to improve its quality and prepare it for further analysis. The Panoptic Region Slicing Segmentation (PRS2) method is then used to emphasize the boundary regions, allowing for more precise localization of melanoma-affected areas. We use Threshold Histogram Evaluation (THE) to assess color variations within segmented regions, which effectively characterizes melanoma-specific color patterns. The features were extracted using Spread Spectral Menzies' Feature Selection (SSMFS), which reduced dimensionality and increased the efficiency of the subsequent analysis. The optimized convolution neural network, derived from the influential AlexNet architecture, serves as the foundation of our proposed approach. By fine-tuning the AlexNet-OCNN on the reduced feature set, we can improve its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN categorizes melanoma classes based on their risk severity, allowing dermatologists to make informed decisions about timely and appropriate treatment. A broad and diversified dataset of dermoscopic images was used in the experimental evaluations. By thoroughly analyzing the melanoma-affected regions, the proposed system performed better than existing methods in terms of detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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Role of internet of things in protecting different wearable gadgets and materials

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Materials Today: Proceedings 2022年 56卷 3387-3393页

作者： Prasant, P. Bhardwaj, Shashank Gupta, Monisha Srivastava, Mahima Singh, Jitendra Maurya, Rajesh Kumar Computer Science and Technology Khalsa College of Engineering and Technology Amritsar India Department of Computer Applications KIET Group of Institutions Ghaziabad India Computer Science and Engineering Chandigarh University Punjab India College Uttarakhand Dehradun India Department of Computer Science Engineering SRM Institute of Science and Technology UP Ghaziabad India MCA ABES Engineering College Ghaziabad India

Out of the most entrusted technological revolutions applied for the advancement of living standards and creation of a more convenient operation in the professional world, the Internet of Things (IoT) has been progressing at the highest projected pace. The interconnectivity of gadgets and devices over the internet to produce smarter control, communication, and a swift and easy lifestyle makes it an amazing tech introduction. However, the IOT also brings along a huge flow of privacy and security concerns whilst considering the huge inflow of data breaches and cyberattacks in the network data. The advancements in the security protocols of the data thus bring along mistrust among the users of IoT networks who might then no longer wish to be a part of this technological revolution. Therefore, it is important to maintain security assurance in the IoT based connection networks of wearable gadgets. This paper discusses the symmetric and asymmetric approaches of encryption over IoT which can help protect the network of smart phones connected to the wearable to assure a safe and smart data commute and avoid access to private data over the same internet connection. © 2021

关键词： Network security

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Generative AI based Secure Wireless Sensing for ISAC Networks

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5195-5210页

作者： Wang, Jiacheng Du, Hongyang Liu, Yinqiu Sun, Geng Niyato, Dusit Mao, Shiwen Kim, Dong In Shen, Xuemin Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology 130012 China Auburn University Department of Electrical and Computer Engineering Auburn United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of University of Waterloo Department of Electrical and Computer Engineering Canada

Integrated sensing and communications (ISAC) is one of the crucial technologies for 6G, and channel state information (CSI) based sensing serves as an essential part of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. Hence, this paper proposes a diffusion model based secure sensing system (DFSS). Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, which guides the ISAC system to appropriately activate wireless links and nodes, ensuring the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only authorized ISAC devices with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to perform the effective sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the illegitimate surveillance. © 2005-2012 IEEE.

关键词： Sensors Wireless communication Diffusion models Wireless sensor networks Communication system security Performance evaluation Security Integrated sensing and communication Fluctuations Accuracy

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Deep Learning Enabled Social Media Recommendation Based on User Comments

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computer Systems science & engineering 2023年第2期44卷 1691-1702页

作者： K.Saraswathi V.Mohanraj Y.Suresh J.Senthilkumar Department of Computer Science and Engineering Government College of EngineeringSalem636011India Department of Information Technology Sona College of TechnologySalem636005India

Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classiﬁcation of this RS research is limited and needs to be *** previous method did notﬁnd any user reviews within a time,so it gets poor accuracy and doesn’tﬁlter the irre-levant comments efﬁ*** Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s *** it is efﬁcient to analyse the trust comment and remove the irrelevant sentence ***ﬁrst step is to collect the data based on the transactional reviews of social *** second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the *** the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the ***,In the classiﬁcation method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the *** simulation results improve the predicting accuracy and reduce time complexity better than previous methods.

关键词： Recommendation systems(RS) social media recursive neural network-based trust recommender system(RNN-TRS) user reviews

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Integration of data science with the intelligent IoT(IIoT):Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon UniversitySeongnamSujeong-gu 13120Republic of Korea University of Electronic Science and Technology of China Chengdu 611731China College of Information Science and Engineering Hohai University213000China Department of Computer Science University of SalernoItaly Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate *** science is the science of dealing with data and its relationships through intelligent *** state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their ***,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various *** paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based *** insights into IoT data security,privacy,and challenges are visualized in the context of data science for *** addition,this study reveals the current opportunities to enhance data science and IoT market *** current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.

关键词： data science Internet of things(IoT) Big data Communication systems Networks Security data science analytics

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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