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

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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Empowering Fake News Detection Through Innovative Hybrid Deep Learning-Based Approach

Empowering Fake News Detection Through Innovative Hybrid Dee...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Suresh, A. Mallika, R.M. Gireesh, S. Singh, G. Hari Kiran Jeevanandham, E.S. Hemalatha, A. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Puttur India Puttur India

ISBN: (纸本)9798331538538

The essence of getting information to the populace in the shortest time possible through social media and other interactive technologies has made it easier for fake news to thrive hence creating a menace to any society as well as its decision-making systems. This research work presents a sound AI-based approach to classify fake news using DL algorithms embedded within a CNN-BiLSTM model. Using the ISOT dataset, the comparison of the outcomes achieved by the proposed model with the original models shows that the proposed model has the highest accuracy score of 94.07%, a precision of 93.50%, a recall of 94.60%, and an F1-score of 94.00%. The proposed hybrid model architecture of CNN for spatial feature extraction and BiLSTM for capturing contextual dependencies that are essential for text analysis and have relatively high accuracy compared to conventional ML algorithms, including CNN, RNN, LSTM, and other DL methods. The proposed approach is verified by a set of comprehensive measures, including ROC curve analysis and various training-validation metrics that reveal the effectiveness of the presented approach in overcoming the challenges of fake news identification. © 2025 IEEE.

关键词： Deep learning

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Hot Deformation Characteristics and Tool Wear Morphology of GH4169 Super Alloy: A Frugal Usage of Cryogenic Cooling During Turning Process

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Journal of The Institution of Engineers (India): Series D 2025年 1-13页

作者： Vinod, B. Sudhakiran, P. Reddy, S. Sunil Kumar Pasupulati, Baburao Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur India Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Department of Electrical and Electronics Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi India

Demand for nickel-based superalloys has increased significantly in the automotive industry because of their great potential to reduce the weight of components and improve efficiency. The present study aims to improve the tribological performance and tool lifespan of the GH4169 alloy through machining, focusing specifically on the potential benefits of using cryogenic cooling. It has better wear and mechanical properties to make this alloy more competitive and viable. Hence, the experiments are conducted under liquid nitrogen (LN2), carbon dioxide (CO2), and minimum quantity lubrication (MQL) conditions. The critical tool wear factors have been investigated. The test results proved that cryogenic cooling with the inclusion of MQL helps improve tool life with better tribological characteristics. Furthermore, a hot deformation test was also performed to identify the material’s behavior. The results indicate that flow curves led to smooth regions controlled at low-temperature and high-strain rates. The formation of DRX structures in the processing map is crucial to improving the mechanical properties of these cast components. The Brinell hardness of nickel-based alloys improved after using cooling conditions. Applying the minimum quantity of lubrication cooling conditions exhibits a high hardness of 498 BHN, a 41% increase compared to other conditions. © The Institution of Engineers (India) 2025.

关键词： Strain rate

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Hybrid renewable energy storage system using modified power management strategies in standalone microgrid

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International Journal of Power and Energy Conversion 2025年第2期16卷 205-220页

作者： Shankar, Gowri Varatharaju, V.M. Subramanian, Sendil Kumar Sathyabhama, B. Siddharth Institute of Engineering and Technology Andhra Pradesh Puttur517583 India Oman Engineering Department University of Technology and Applied Sciences Ibra Oman Department of Electrical and Electronics Engineering S.A. Engineering College Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Bangalore Trunk Road Varadharajapuram Poonamallee Tamil Nadu Chennai600123 India

The utilisation of renewable power sources has been more common as a dependable and clean energy source. Two possible alternative sources of energy include renewable energy sources, such as solar energy and wind energy. The audit para monitoring system (APMS) and the versatile energy spectral algorithm (VESA) control based hybrid system that has been discussed have been built with the support of the MATLAB simulation 2017b software. These characteristics include switching losses, electromagnetic interference, efficiency, power factor, and total harmonic distortion. In the proposed system, the average voltage may be generated with a total harmonic distortion (THD) of 4.73% and a switching loss of 3.8%. The proposed VESA was able to produce outcomes that were better. A total harmonic distortion (THD) of 4.09% and a switching loss of around 3.5% are both characteristics of the voltage that it generates. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Hybrid power

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Empowering Fake News Detection Through Innovative Hybrid Deep Learning-Based Approach

Empowering Fake News Detection Through Innovative Hybrid Dee...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： A Suresh R M Mallika S Gireesh G Hari Kiran Singh E S Jeevanandham A Hemalatha Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India Department of Computer Science and Engineering (AI&DS) Siddharth Institute of Engineering & Technology Puttur India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

关键词： Fake news Accuracy Feature extraction Bidirectional long short term memory Convolutional neural networks Data models Computer science Vectors Computational modeling Adaptation models

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SDIRTCC: Secure duplicate Identification and removal from textual data in cloud computing

SDIRTCC: Secure duplicate Identification and removal from te...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： TMS. Mekalarani M. Giri D. Subhan B. Sai Kumar K. Sunilkumar Reddy B. Shilpa Department of CSE Siddharth Institute of Engineering and Technology Puttur Andhra Pradesh India Department of CCC Siddharth Institute of Engineering and Technology Puttur Andhra Pradesh India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In the world day by day textual information size is increased exponential and in particular, vision, analysis and interpretation of language force a challenge of management of data, in a big storage with heavy storage space. In order to minimize data size eliminate duplicate is one of the way to follow and duplicate types are also forces security problems. In this research a new method secure duplicate packet and elimination from textual dataset in a cloud computing (SDIRTCC). It is working in hybrid model by combining eliminate duplication from user side and as well as from server side to get good rate of compression and provided full security to data. SDIRTCC is small techniques preprocess data at user side to identify and eliminate duplicate entries and this is well suitable method for devices like IoT working under constraints, and also designed to protect from side channel attack. Touchdown(TD) dataset is used to conduct experiment. It consists of running the written by peoples. SDIRTCC shows 72% of compression rate that in turn shows the impact of duplicate data present in the dataset, and reduced TD dataset occupies less storage when compared with original TD dataset. Finally, reducing dataset size demands less storage space in cloud, saved lot of space in cloud, easy to maintain security, and enhanced efficiency and management of big data in cloud computing.

关键词： Cloud computing Computational modeling Scalability Memory management Side-channel attacks Data models Security Object recognition Servers Industrial Internet of Things

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Blockchain: Development of New Cloud Repository Model using Federated Learning Techniques

Blockchain: Development of New Cloud Repository Model using ...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： B. Sarvesan M. Giri P. Gayathri V. Teja N. Jeevan Kumar C. Chandra Sekhar Department of CSE Siddharth Institute of Engineering and Technology Puttur Andhra Pradesh India Department of CCC Siddharth Institute of Engineering and Technology Puttur Andhra Pradesh India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The advancement of IoT are widely used in Medical sector, merging IoT with medical domain and introduced IoMT, it provides new challenging task to provide integrity and security, controlling and managing and predicting data that is gathered through IoMT. In his paper proposed a new security cloud storage model by combining features of block chain technology and federated learning. In order to provide security proposed model gathers medical data using IoT, collected data is encoded using attribute encryption method (AEM) using a key Hybrid Energy Optimizer (HEO) method, with permission encoded data stored in a block chain, and predicts IoMT data from threats. Security using federated learning methods like GRU and BiLSTM are combined to improve prediction accuracy and patient are predicted from various types of disease. In his model patients data trained using DL methods, and privacy is maintained using collaborative methods. From experiments we come to know that our methods shows more accuracy they traditional methods.

关键词： Support vector machines Cloud computing Accuracy Federated learning Bidirectional long short term memory Predictive models Data models Blockchains Security Diseases

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems

Secure and Efficient Deduplication for Multimedia Sharing in...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： A. Suresh Erasappa Murali V. Anjali Sri E Sumana Gopaluni Venkata Sai Sathwik P. Devendra Reddy Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48 % average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments.

关键词： Resistance Cloud computing Data privacy Protocols Scalability Semantics Throughput Solids Vectors Encryption

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An Efficient Deep Learning based Waste Management System for Sustainable Environment 3

An Efficient Deep Learning based Waste Management System for...

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

作者： Saghana, K. Saranya, P. Mahesh Reddy, A. Keerthy Rai, V. Ramasubramanian, B. Sudhakaran, P. Department of ECE Sri Venkatesa Perumal College of Engineering and Technology Andra Pradesh Puttur India Department of ECE Sri Venkateswara College of Engineering and Technology Andra Pradesh Puttur India Department of ECE Siddharth Institute of Engineering and Technology Andra Pradesh Puttur India Department of ECE SRM TRP Engineering College Tamil nadu Tiruchirappalli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Efficient Management of solid and liquid waste is crucial for maintaining environmental sustainability and public health. This work presents a novel approach for the detection and segregation of solid and liquid waste using deep learning techniques. By leveraging Convolutional Neural Networks (CNNs), a system is designed which is capable of automatically classifying waste into solid or liquid categories based on real-time image data from waste disposal units. The system also integrates Internet of Things (IoT) devices to transmit the classified waste data to a remote central collection centre for optimized disposal processes. The model was trained on a diverse dataset of waste images and achieved high accuracy in distinguishing between solid and liquid waste. Once classified, the waste information is communicated via a secure wireless network to the central collection centre, which can coordinate timely collection and disposal efforts. This approach not only automates the waste management process but also reduces manual errors and delays in waste segregation and disposal. This work also discusses the potential for scalability and implementation in smart city infrastructures. Compared to the state of art method existing in the literature, this proposed method achieves an accuracy of 98.31 %, Precision of 97.93%, recall of 98.17% with a F1- score of 0.98. This system demonstrates the effectiveness of deep learning in waste classification and proposes a scalable solution for enhancing waste management practices, ultimately contributing to a more sustainable environment. © 2025 IEEE.

关键词： Convolutional neural networks

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Efficient Grid and Density based EGDBSCAN Clustering Method to Identify Global Noisy Points 4

Efficient Grid and Density based EGDBSCAN Clustering Method ...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Babu, S. Mallesh Sharma, V.K. Giri, M. Department of CSE Bhagwant University Rajasthan Ajmer India Department of ECE Bhagwant University Rajasthan Ajmer India Department CSE Siddharth Institute of Engineering and Technology Andhra Pradesh Puttur India

ISBN: (纸本)9798331530402

Clustering methods are used in many applications like psychology, computers, biology, and so on. DBSCAN is a one of the clustering algorithm which comes under density based clustering technique. DBSCAN algorithm clustering data samples based on density values, but all clustering techniques are proposed with various types of challenges. DBSCAN clustering method is also fail to detect all noisy points. In this research paper we a proposed a new novel efficient DBSCAN (EGDBSCAN) algorithm to cluster data set. First data samples are placed into grid cells, for each cell calculate LNPS, MLNPS, and NNPG values, all new data samples are added one by one to grid space and update the above mentioned parameters, and finally identify list of top 'k' global noisy points. Performance of proposed clustering is compared with KNN noisy detection method and we come to know that proposed algorithm shows better results. © 2025 IEEE.

关键词： K-means clustering

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