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Synthesis and characterization of PET impregnated agro-medical waste: Novel approach for plastic substitute using hybrid composites

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Materials Today: Proceedings 2024年 98卷 54-62页

作者： B. Vinod S. Suresh S. Sunil Kumar Reddy K. Sai Sujith Department of Mechanical Engineering Siddharth Institute of Engineering &Technology Puttur 51758 AP India

Composites are environmentally friendly materials made from natural fibers that have lightweight properties and improve the performance of synthetic parts. Medical waste has been identified as one of the biggest challenges recently in India due to COVID-19. More than 70% of medical waste is considered non-hazardous waste, obtained from crushing used surgical face mask (SFM) and surgical gloves (SG) as reinforcement materials chosen in this study. The research aim is to increase the strength and minimize the cost of production of polyethylene terephthalate (PET) matrix by employing hybrid waste fibers as particle reinforcement particles. Five fiber composites of 3% to 12% weight fractions of hybrid fiber (Kevlar Fiber/Glass fiber) and a constant 10% medical waste were fabricated using an injection moulding process to regulate the polymer structure and determine its thermal conductivity . Adding fibers improved mechanical properties such as tensile, flexural, and impact strength . TGA and thermal conductivity were analyzed. Tribological characteristics like a specific wear rate and coefficient of friction were conducted. Morphology is used to identify material behaviour by applying different weight fractions. Adding 9% (KF/GF) and 10% (SFM/SG) particles improves the wear and mechanical properties of the hybrid composite by 52%, respectively. Adding medical and fiber waste particles will improve the interfacial bonding between the matrix material and fillers. It is widely used as a leaf spring in ballistic and automobile applications suspension.

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Intelligent Home Security System Driven by IoT 10

Intelligent Home Security System Driven by IoT

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Veera Boopathy, E. Karthikkumar, S. Kannadhasan, S. Raghul, G. Niranjan, S. Gajendiran, Kiruba Shankar Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Karpagam Academy of Higher Education Department of Civil Engineering Coimbatore India Study World College of Engineering Department of Electronics and Communication Engineering Coimbatore India Siddharth Institute of Engineering & Technology Department of Electronics and Communication Engineering Puttur India

ISBN: (纸本)9798350353778

Modern-day living has become more dependent on intelligent security systems. The suggested safety structure was developed with fewer devices and a stronger, independently protected device that includes intrusion detection and gate safety. Face recognition technology is used for gate security, allowing only those granted permission to enter that area. Instead of employing sensor equipment, identifying intruders is accomplished by carrying out image manipulation on recorded frames of information and determining the difference between the currently attracted images and the operating enclosures that exist as images in the attracted plans. Intelligent safety infrastructure has become increasingly important in modern life. The suggested security system was designed for more robust autonomous protection technology to provide surveillance and gate protection. The entrance locking system applies facial recognition technology, enabling only authorized persons to enter that region. Instead of employing sensor equipment, identifying intruders can be accomplished through visual analysis of recorded frames of information while determining the difference between the just-defined images and the incoming enclosures captured in the diagrams. Using a Raspberry Pi development board, this standalone safety device was developed with a rechargeable power source, a WiFi connection, and an external USB port. Security-ready emails sent through the nearby mailbox of the police office have enabled automatic e-complaint enrollment. Compared to other current structures, this one is more effective, sturdy, and consumes significantly less data and electricity. © 2024 IEEE.

关键词： Enclosures

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Sub-optimal antenna selection technique over Weibull-Gamma fading channel for MIMO communication systems

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 95-101页

作者： Damodaran, Selvam Paranche Perumal, Vijayakumar Verappan, Ganesan Department of Electronics and Communication Engineering Siddharth Institute of Engineering and Technology Andhra Pradesh Puttur India Department of Electronics and Communication Engineering VIT University Tamil Nadu Chennai India Department of Electronics and Communication Engineering Bharath Institute of Higher Education and Research Tamil Nadu Chennai India

In this paper, the orthogonal space-time block code (OSTBC) of the multi-input multioutput (MIMO) system is considered. The performance of a MIMO communication system is assessed using antenna selection techniques (AST) over the Weibull-Gamma fading (WGF) channel. The channel state information on the transmitter side (CSIT) can be utilised to improve the system capacity and error rate at the same time with reduced complexity of the hardware. The CSIT in AST is used for OSTBC of the MIMO system over the WGF channel to improve the bit-error rate (BER) of the system. The SNR performance improvement is discussed over Weibull and gamma fading parameters. In this paper, both optimal and suboptimal AST analysis is derived for various numbers of antennas and capacity improvement of the channel is analysed. © 2023 Inderscience Enterprises Ltd.

关键词： Channel state information

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"performance Emission Parameters Behavior of a di Diesel Engine Using Al2O3and TiO2Nano Additive Biodiesel Blends"

"performance Emission Parameters Behavior of a di Diesel Eng...

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SAE International Conference on Advances in Design, Materials, Manufacturing and Surface engineering for Mobility, ADMMS 2023

作者： Anchupogu, Praveen Krupakaran, R.L. Ganipineni, Jamuna Rani Petla, Ratna Kamala Bapatla Engineering College India Mohan Babu University India V R Siddhartha Engineering College India Siddharth Institute of Engineering & Technology India

This study mainly focuses on the blending of Alumina and Titanium oxide nanoparticles (NP's) in Spirulina biodiesel blends (SB20) to estimate the influence of engine (combustion, performance and emission) parameters of a diesel engine. The characterization of Al2O3 and TiO2 NP's like SEM were reported. By using various fuel samples such as Diesel, SB20, SB20+40 ppm AO, SB20+80 ppm AO, SB20+40 ppm TO and SB20+80 ppm TO, the engine tests on the diesel engine were conducted at various load conditions. The BTE for SB20+80 ppm AO were enhanced by 12.35% and 8.4 % compared to the SB20 fuel and SB20+40 ppm AO fuel samples. The combustion parameters were improved for the NP's as additives (Al2O3 and TiO2) fuels than the SB20 fuel sample because NP's contain oxygen content. The parameters of engine exhaust emissions such as HC, CO and smoke are drastically diminished for the SB20+40 ppm AO, SB20+80 ppm AO, SB20+40 ppm TO and SB20+80 ppm TO fuels compared to the SB20 fuel. A marginal improvement was observed for the NP's blended fuel samples in case of NOx emissions than the B20 fuel. © 2023 SAE International. All Rights Reserved.

关键词： Titanium dioxide

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Hybrid Filtering-Based Product Recommendation System Integrating GRU and BFGS Optimization

Hybrid Filtering-Based Product Recommendation System Integra...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： A. Suresh R. G. Kumar D. Nagaraju K D Mohana Sundaram B Anandan Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India Department of ECE Siddharth Institute of Engineering & Technology Puttur India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

It is more crucial than ever to handle crucial problems including data sparsity, cold-start problems, and the requirement for extremely precise forecasts in today’s ever changing recommendation system field. This work presents a hybrid filtering product recommendation system that integrates Gated Recurrent Units (GRU) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method to significantly enhance recommendation accuracy and performance. Standard techniques such content-based filtering and collaborative filtering can fail to offer reliable recommendations for large and dynamic datasets. Our hybrid model uses BFGS optimization to increase model accuracy and efficiency while utilizing GRU-based collaborative filtering to capture sequential user-item interactions. The GRU-BFGS model outperforms matrix factorization (MF) techniques in numerous areas, achieving an astounding accuracy of up to 92% on the Amazon customer review dataset. It also exhibits excellent recall, precision, and notable decreases in error metrics (MAE, MSE, and RMSE), which makes it a very successful method for providing individualized, accurate suggestions.

关键词： Training Accuracy Filtering Reviews Computational modeling Collaborative filtering Optimization methods Predictive models Real-time systems Recommender systems

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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

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.

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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Detection of Parkinson's Disease using Machine Learning with Feature Analysis from Audio Signals

Detection of Parkinson's Disease using Machine Learning with...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Kuruma Purnima M.A. Manivasagam Dudekula Mohammed Kaif M Vanitha Nagella Rajesh B Silambarasan Shaik Suhail G Ravi Kumar V Hanumantha Rao Department of CSE Siddharth Institute of Engineering & Technology Puttur India Department of CSE Siddharth Institute of Science & Technology Puttur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Background: Early detection of Parkinson's Disease (PD) is crucial for timely intervention and improved patient outcomes. PD affects motor functions and often results in vocal impairments, making voice analysis a promising non-invasive diagnostic approach. Methods: This study evaluates the effectiveness of machine learning algorithms—Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and XGBoost—for PD detection using a dataset of vocal features from the UCI Machine Learning Repository. The dataset, comprising 23 vocal attributes, was preprocessed using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Models were assessed using metrics such as Accuracy, F1-Score, Recall, Precision, and ROC-AUC. Results: The Random Forest and Support Vector Machine algorithms demonstrated the highest classification accuracy (96.6%) and superior overall performance. These findings highlight the potential of these methods for distinguishing between PD-positive and PD-negative cases using voice data. Concluding Remarks: This study underscores the potential of machine learning in developing accessible and accurate tools for early PD diagnosis. Further enhancements, such as feature refinement and dataset expansion, could improve these methods' generalizability and robustness, supporting their integration into healthcare systems.

关键词： Support vector machines Wireless communication Analytical models Accuracy Machine learning algorithms Parkinson's disease Machine learning Medical services Feature extraction Random forests

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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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