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

作者： Daniel, E. Akilan, L.H. Infant, P. Alwin Jainish, G.R. Panimalar Engineering College Department of Computer Science & Engineering Chennai City Campus Chennai India Loyola Icam College of Engineering and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350376425

The alarming rise in motorcycle accidents worldwide necessitates the development of effective safety measures. This research introduces a novel helmet identification system utilizing Single Shot Detection (SSD) algorithm and Convolutional Neural Network (CNN) technology. The proposed system combines feature extraction and object identification techniques to accurately determine whether riders are wearing helmets in real-time. By integrating helmet detection with ignition control, this system actively prevents motorcycles from starting without a helmet, ensuring enhanced rider safety. Experimental results demonstrate the effectiveness of the CNN-SSD based helmet detection system, achieving minimal false positives and high detection accuracy, making it suitable for real-world deployment. This innovative approach contributes to road safety and reduces the risk of injuries associated with motorcycle accidents. © 2024 IEEE.

关键词： Motorcycles

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Network Media Platform to Build a Sustainable Development of Ecological Environment Using Blockchain technology 3

Network Media Platform to Build a Sustainable Development of...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sundar, G. Patchaiammal, P. Swetha, V.P. Rengarajan, Manikandan Satheesh, Nimmati Sasikala, P. Sindhi College Department of Computer Applications Chennai600077 India Panimalar Engineering College Department of MBA Chennai India RandD Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Avadi Chennai6000627 India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Department of Computer Science Bangalore India

ISBN: (纸本)9798350396157

Social technology has grown rapidly and effectively as a result of new information technology. The importance of the big data approach, which was among the most prominent current occurrences, has improved as a result of the amount of information generated from social media. For the installation of renewable energies, the Energy Network seems to have become a central feature. Because there are so many different sustainable energy sources and how they interact with the grid, the system is extremely complex and presents a number of intrinsic challenges, such as the coordination and monitoring of distributed sustainable energy generation, which can be extremely difficult to handle. This study presents a blockchain-based approach for managing digital information in a network as a solution to these problems. Blockchain is a distributed ledger that implements agreement, inevitability, and transparency for online data using encryption schemes, hash chains, and consensus mechanisms. The suggested approach can employ blockchain's features to achieve efficient network content creation management, copyright management, system administration, and user instructional techniques. Our suggested method can consistently provide the network media platform with crucial assistance, enabling it to pursue long-term, ecologically sustainable expansion. © 2024 IEEE.

关键词： Smart contract

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Reversible data hiding in encrypted images with block-based bit-plane reallocation

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Multimedia Tools and Applications 2024年第37期83卷 84911-84932页

作者： Liu, Li Chen, Chaofan Wu, Yingchun Chang, Chin-Chen Wang, Anhong College of Electronic Information and Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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Protecting Recommendations: Combating BIAS and Manipulation in Recommendation 8

Protecting Recommendations: Combating BIAS and Manipulation ...

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

作者： Infant, P.Alwin Kumar, P.Mohan Jainish, G.R. Hindustan Institute of Technology & Sciencce Department of Computer Science and Engineering Chennai India Loyola Icam College of Engineering & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Recommender systems assist consumers in navigating the deluge of information by helping them find services and goods. The effectiveness of recommender systems has been thoroughly examined in research currently available from a number of angles. In the research community, robustness against malicious attack is becoming a more important concern. Collaborative Filtering (CF) recommender systems have been established to accomplish this objective. However, due to the fact that they are collaborative recommender systems are open to attack. To weaken the system, attackers can introduce fictitious and biased profiles. An examination of the random attack, Bandwagon attack, Mixed Attack and Noise Injection on recommender systems is presented in this work. Numerous algorithms are susceptible to profile injection attacks. Some of the earlier approaches suffer from only looking at one aspect of user rating values while assessing the rating matrix, ignoring alternative viewpoints. First, a rating matrix in this research that takes into account both genuine and fictitious user ratings is created. Next, using user rating time as a basis, the rating matrix is examined. Next, build a model to distinguish between real and fraudulent users using a variety of classifiers, including Principal Component Analysis (PCA), K-Nearest Neighbour (KNN), and Bayesian Networks (BN). Ultimately, a number of tests are conducted, and a summary of the findings comparing different approaches' abilities to detect small-scale attacks and detect overall attacks is presented. © 2024 IEEE.

关键词： Collaborative filtering

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Optimal Deep Convolutional Neural Network with Pose Estimation for Human Activity Recognition

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computer Systems science & engineering 2023年第2期44卷 1719-1733页

作者： S.Nandagopal G.Karthy A.Sheryl Oliver M.Subha Department of Computer Science and Engineering Nandha College of TechnologyErode638052TamilnaduIndia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science and Engineering St.Joseph's College of EngineeringChennai600119TamilnaduIndia Department of Electronics and Communication Engineering University College of Engineering NagercoilNagercoil629004TamilnaduIndia

Human Action Recognition(HAR)and pose estimation from videos have gained signiﬁcant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,*** considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difﬁcult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so *** the literature,it is observed that hard to deal with the temporal dimension in the action recognition *** neural network(CNN)models could be used widely to solve *** this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity *** KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose *** the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human ***,an optimal DCNN model is developed to classify the human activities label based on the extracted key *** improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch *** experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.

关键词： Human activity recognition pose estimation key point extraction classiﬁcation deep learning RMSProp

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Microstructural Analysis of Ceramic Materials Through Machine Learning Techniques: A Benchmarking Interdisciplinary Review for Ceramic Material Characterization

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Journal of The Institution of Engineers (India): Series D 2025年 1-19页

作者： Roy, Mahuya Kumar, Bijendra Chowdhury, Soumit Bhattacharya, Tapas Kumar Maity, Satyabrata Haldar, Partha Department of Computer Science and Engineering St. Thomas College of Engineering and Technology West Bengal Kolkata700023 India Gun and Shell Factory West Bengal Cossipore Kolkata700002 India Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India Department of Ceramic Technology Government College of Engineering and Ceramic Technology West Bengal Kolkata700010 India Department of Information Technology Techno International New Town 1/1 Service Rd. DG Block New Town Kolkata700156 India Department of Mechanical Engineering Government College of Engineering and Ceramic Technology West Bengal Kolkata700010 India

This study presents a revolutionary analytical study on ceramic microstructural images in the context of sophisticated image processing as well as machine learning techniques for automated and most accurate characterization of ceramic material properties. The main objective here is to scientifically correlate the derived microstructural image features with the concern material properties and this can be purely governed by specific material compositions that lead to better productivity. So, this particular unique study purely focuses on some efficient machine learning algorithms like K-means and fuzzy C-means in contrast to the traditional image processing concepts like Canny and Sobel for most effective classification as well as automated evaluation of ceramic microstructural image features. In addition, more advanced deep learning techniques like convolutional neural network also reflected here with in depth comparative analysis in view of its superiority for crack estimation, pore characterization, phase identification, grain boundary detection and grain area calculation that leads to accurate material characterizations. Further, continuous up gradations are still highlighted here with recent advanced deep learning models like Auto encoders, generative adversarial networks and recurrent neural networks for much more accurate characterization of ceramic microstructural image features boosting both material design and productivity in great manner. Apart from that, some latest image property related transformations are also investigated here for accurately tracing the critical image features while this study even adopts some exhaustive bibliometric analysis to precisely identify the effective microstructural evaluation methods from the existing literature. Hence this novel study not only provides a concrete pathway for identifying the most effective mechanism for ceramic microstructural image property evaluation and characterization in the present scenario but it als

关键词： Ceramic materials

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On the Complexity Aspects of Various Sub-classes of Linear Languages 9

On the Complexity Aspects of Various Sub-classes of Linear L...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Praveen, G.L. Sunil, K.S. Mar Baselios College of Engineering And Technology Department of Computer Science & Engineering Kerala Thiruvananthapuram India Government Engineering College Idukki Department of Computer Science & Engineering Kerala Painavu India

ISBN: (纸本)9798350394474

The non-terminals in the underlying grammar play an important role in determining different characteristics of the language generated. The languages generated by the grammars are determined by a unique non-terminal known as the start symbol. Even without altering any of the production rules, the grammar produces a distinct language if we swap the start symbol for any other non-terminal. Occasionally, we purposefully include extra non-terminals to attain specific characteristics in both the grammar and parse trees. One such scenario is when we convert a language to Chomsky's Normal Form (CNF), in which case we add additional non-terminals to convert the parse tree into a binary tree. In this paper, we consider different sub classes of CFLs by restricting the number of nonterminals. We study different characteristics like the generative power of the grammar, the stack height, and the number of turns of the PDA which accept the languages produced by the grammars. We show that these characteristics are influenced by the number of nonterminals in the grammar. © 2024 IEEE.

关键词： Binary trees

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Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

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Digital Communications and Networks 2025年第3期11卷 912-924页

作者： Chen, Zhuo Yi, Jiahuan Zhou, Yang Luo, Wei College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Department of Computer Science and Software Engineering Auburn University Auburn United States Yunnan Aerospace Engineering Geophysical Detecting Co. Ltd. Kunming650217 China

Blockchain technology, based on decentralized data storage and distributed consensus design, has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), it optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms. © 2024 Chongqing University of Posts and Telecommunications

关键词： Reinforcement learning

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A Machine Learning and Deep Learning based Approach to Generate a Speech Emotion Recognition System 18

A Machine Learning and Deep Learning based Approach to Gener...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sumera Vaidehi, K. Nisha, Qamar Stanley College of Engineering and Technology for Women Department of Artificial Intelligence & Data Science and Computer Engineering Hyderabad India Stanley College of Engineering and Technology for Women Department. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The most common and general medium via which we humans convey or communicate our thoughts, emotions, feelings or ideas artlessly is by speech or articulation. Blending of this artless way of speech with the technological advancements of AI, has given rise to the importance of building emotion recognition systems from speech today. Even more, the speech/articulation emotion recognition system presented here is also to contribute in and facilitate various emerging applications of today like, in detecting persons' physiological state (as in lie detectors), also be used in forensics, medicine. The proposed work identifies/associates an appropriate label/emotion for the respective emotion from speech presented in the form of an audio file (.wav format). About 4240 audio samples are taken. 1440, 2800 samples from RAVDESS and TESS datasets are considered respectively. After this process of Data collection, features are separately extracted for each audio dataset mentioned above. Energy, pitch, ZCR, co-efficient of Mel frequency ceptrum (MFCC) are some of the features considered in this study. Furthermore, clubbing and merging of 2 datasets is performed resulting in a total of 4240 rows and 24 columns (features/characteristics including 1class label) of audio samples. The resulting 4240 samples of feature dataset is split/bifurcated into training and testing set by considering 3 different possibilities/instances viz;60%-40% ratio, 70%-30% ratio, 80%-30% ratio. The models namely CNN, Random forest and Support Vector Machine are trained to classify the dataset into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). An attempt to implement the models using two very essential disciplines of AI i.e. Machine Learning and Deep Learning is made here. The accuracy or results are depicted by generating confusion matrices on test data for CNN, RF and SVM models (Each model is trained and test across 3 different ratios viz;60%-40%, 70%-30%, 80%-20%). C

关键词： Classification CNN Confusion matrices Extraction of features MFCC RF Speech/articulation Emotion Recognition SVM

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