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Power and Energy Conference at Illinois (PECI)

作者： H. S. Sunil Kumar R. B. Anand K. R. Channakeshava T. Rangaswamy B.S. Shobha Department of Mechanical Engineering D.A.C.G. (Govt.) Polytechnic Chikmagalur INDIA Department of Mechanical Engineering National Institute of Technology Tiruchirappalli INDIA Department of Mechanical Engineering Government Polytechnic Holenarasipura INDIA Department of Mechanical Engineering Government Engineering College Hassan INDIA

The present paper investigates the evaluation of flutter analysis of horizontal axis (100m) wind turbine blade (HW AT), with DU-97-W-300 aerofoil cross section using mathematical model supported by standard Finite Element Analysis. The FEA codes along with PK - method is carried out for finding flutter solutions whereas Double Lattice Method is used for finding matrices and aerodynamic loads. Galerkian equations are utilized to solve the problems associated with the equations of motion. The study analyses the coupled mode flutter of rigidly fixed, flexible aerofoil. The results presented in this paper shows the significant impact of first torsion and third flapwise bending modes in the motion of blade structure. Further the study focuses on the improper isolation of torsional natural frequencies which resulted in poor flutter performance of the blades associated with the lowering of critical velocity into the operational ranges.

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The Role of Internet of Things (IOT) for Cloud Computing Based Smart Grid Application for Better Energy Management using Mediation Analysis Approach 2

The Role of Internet of Things (IOT) for Cloud Computing Bas...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Sathishkumar, R. Mehdi Hassan, Abu Md. Mohseni, Milad Tripathi, Amrendra Tongkachok, Korakod Kapila, Dhiraj SRM Trp Engineering College Department of Eee Tamilnadu Trichy India Govt. City College Chittagong Department of Physics Bangladesh Tabriz Iran School of Computer Science Upes Dehradun India Thaksin University Faculty of Law Thailand Lovely Professional University Department of Computer Science & Engineering Phagwara India

ISBN: (纸本)9781665437899

Cloud computing can be considered as a key tool in the energy management process that reflects effective monitoring, controlling as well as conserving energy. Energy management is a key concern in pepper implementation of smart grid technology. Due to the absence of cloud computing, energy cannot be supplied to a broad range of users as limited storage and memory are present in traditional technology. In the traditional energy management process, various kinds of stability issues can be seen in the absence of any cloud. Concerning all those reasons cloud computing is used in smart grid technology for providing a broad range of energy without interrupting the power supply. On the other hand, smart grid technology reflects a unique network that generates a two-way electricity flow by using various kinds of digital tools and techniques. This innovative technology can easily detect and at the same time react against any kind of issue generated in this technology due to the presence of cloud computing techniques. Compared to traditional technology, a huge level of renewable energy resources are used in smart grid technologies. A less amount of carbon emission reflects the generation of a better future environment. A survey analysis has occurred by taking 51 random people and three questions are asked related to effectiveness of cloud computing on smart grid technology and impact of this technology on the environment. A detailed discussion has also given after analysing survey. © 2022 IEEE.

关键词： Internet of things

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Comparative Analysis of Charge Plasma Vertical TFET Structures with HfO2 and SiO2 Gate Dielectric materials

Comparative Analysis of Charge Plasma Vertical TFET Structur...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Bharathi, B. Krishna Ramanjaneyulu, N. Jntua Department of Ece Andhra Pradesh Anantapuramu515002 India Govt. Polytechnic AndhraPradesh Tadipatri515411 India Rajeev Gandhi Memorial college of Engineering and Technology Department of Ece Andhra Pradesh Nandyal518501 India

ISBN: (纸本)9798331527495

This study investigates the impact of different gate dielectric structures on the performance of charge plasma vertical tunnel field-effect transistors (TFETs). Three TFET configurations were considered: a single material gate (H fo2), a double material gate (H fO2, SiO2), and a stacked gate (H fO2 stacked on SiO2). All of the three structures are evaluated using lateral and vertical conduction currents, electron and hole mobilities, and quasi-Fermi levels. The results indicate a major improvement in the devices' performance with the presence of extra dielectric layers in the double and stacked gate structure. The proposed structures allow peak currents, superior carrier mobilities, as well as even more favorable tunneling barriers when compared to the single material gate. The findings suggest that the careful engineering of gate dielectric materials and their stacking can optimize the performance of TFETs, paving the way for their potential applications in future high-performance electronic devices. © 2025 IEEE.

关键词： Tunnel field effect transistors

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Healthcare Data Management Optimization Using LSTM and GAN-Based Predictive Modeling: Towards Effective Health Service Delivery 2

Healthcare Data Management Optimization Using LSTM and GAN-B...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Changala, Ravindra Kaur, Chamandeep Satapathy, Nr. Rutuparna Vuyyuru, Veera Ankalu Santosh, Kathari Valavan, M. Ponni Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Jazan University Department of Computer Science Jazan Saudi Arabia Govt. of Odisha Health & Wellfare Department Balasore India Koneru Lakshmaiah Education Foundation Department of Cse Andhra Pradesh Vaddeswaram India Cmr Institute of Technology Department of Mba Bengaluru India K. Ramakrishnan College of Engineering Department of Ai & Ds TamilNadu Trichy India

ISBN: (纸本)9798350373110

In the rapidly evolving landscape of healthcare, effective data management plays a pivotal role in enhancing health service delivery. This study explores the application of Long Short-Term Memory (LSTM) and Generative Adversarial Network (GAN) based predictive modelling to optimize healthcare data management. The proposed framework leverages LSTM to capture temporal dependencies in healthcare data, allowing for improved prediction of patient outcomes and resource utilization. GANs are employed to generate synthetic healthcare data. The primary objectives of this research include the development of an advanced predictive modeling system for healthcare data and the optimization of health service delivery processes. By harnessing the power of LSTM and GANs, the study aims to address challenges related to data heterogeneity, imbalances, and scarcity. The proposed LSTM-GAN model shows better accuracy with 98.5% which is 4 % higher when compared with SVM and GRU. The implications of this work extend beyond predictive modeling, influencing policy-making and strategic decision processes in the pursuit of effective, accessible, and patient-centric healthcare services. © 2024 IEEE.

关键词： Electronic health record

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Preparation, Structure analysis and Antibacterial Properties of Iron Complexes mixed with 8-Hydroxyquinoline and Some Amino Acids

Preparation, Structure analysis and Antibacterial Properties...

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Smart Devices (ICSD), International Conference on

作者： M. Amin Mir Minakshi Memoria Anuj Kumar Shailendra Prakash Anita Bisht Samar Dernayka Kim Andrews Department of Mathematics & Natural Sciences Prince Mohammad Bin Fahd University AL Khobar Saudi Arabia Department of Computer Sciences and Engineering Uttaranchal University Dehradun Department of Chemistry Govt. (PG) College Kotdwar Pauri Uttarakhand Department of Chemistry Govt. (PG) College Jaiharikhal Uttarakhand Department of Civil Engineering Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

The 8-hydroxyquinoline (Q) has been used as a primary ligand in diverse ligand complex synthesis of Iron (II) and [Fe(Q)(L)2H2O] complex types, and amino acids (L) such serine, phenylalanine, and L-leucine have been used as secondary ligands. Analysis such as electrical conductance, magnetic susceptibility, spectroscopic and thermal studies have been used in metal complex analysis. The complexes’ electrical conductance investigations show that they are not electrolytic in origin. Measurements of magnetic susceptibility showed that the complexes were diamagnetic. Ligand charge transfer transition states can be observed in electronic absorption spectra. Proton (NMR)chemical environments are validated in addition to FT-IR studies demonstrating metal ion bonding through the ligand N- and O-donor atoms. Furthermore, analysis of thermal results show that coordinated water molecules are present. An agar cup and tube dilution procedure were used to investigate antibacterial activity of the complexes alongside the pathogenic bacterium P. aeruginosa, S. epidermis, P. vulgaris, and E. faecalis.

关键词： Temperature measurement Protons Atoms Antibacterial activity Ions Epidermis Iron Thermal analysis Charge transfer Magnetic susceptibility

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A Novel Single Shot Multibox Detector based Face Mask Detection

A Novel Single Shot Multibox Detector based Face Mask Detect...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer Department of Computer Applications Govt. Engineering College Bikaner India

Face mask detection has gained significant importance in recent times due to the COVID-19 pandemic. This research work proposes a face mask detection method using the Single Shot MultiBox Detector (SSD) framework. The proposed method aims to accurately identify whether individuals are wearing masks or not in real-time scenarios. To begin with, a dataset comprising images of masked and unmasked faces is collected from kaggle and annotated with corresponding labels. The SSD model is then trained on this dataset, utilizing its ability to simultaneously perform object localization and classification. By leveraging the pre-trained weights and the architecture of SSD, the model is able to detect faces and classify them as either wearing masks or not. During the inference phase, the trained SSD model is applied to input images or video frames. The model analyzes the detected faces and assigns a label based on the presence or absence of masks. The output includes bounding box coordinates around the detected faces, along with the corresponding mask or non-mask label. Experimental evaluations are conducted on various real-world datasets to assess the performance of the proposed method. The results demonstrate the effectiveness and efficiency of the approach, achieving high accuracy in face mask detection.

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A study on fatigue behaviour of cotton/lycra core spun yarns

Textile and Leather Review

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Textile and Leather Review 2022年 5卷 484-496页

作者： Nurussaba, Kasimi Sinha, Avishek Bhatia, Dinesh Sachdeva, Anish Sinha, S.K. Department of Textile Technology Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Textile Engineering Jawaharlal Nehru Govt. Engineering College Himachal Pradesh Sundernagar India Department of Industrial and Production Engineering Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

Core spun yarn gives denim fabrics durability and flexibility, which has increased the focus on these yarns among denim manufacturers in recent years. These yarns undergo tensile fatigue loading, a typical phenomenon that happens on various parts of clothing, during use and wear. The study on the response of yarns during cyclic loading is essential because it leads to a better understanding of the growth characteristics when repeatedly loaded during the use of core spun yarn fabrics. In this study, the impact of various production parameters such as linear density, stretch percentage, twist multiplier, and the number of cyclic loading on the fatigue value of core-spun yarns was investigated. 18 different core spun yarns were manufactured by modified ring spinning to investigate the effect of various production parameters on fatigue value before and after 10, 20, and 30 load cycles. Experimental results showed that there is an increase in fatigue value with an increase in the number of load cycles. Statistical analysis showed that there is a significant effect of linear density, stretch percentage, and twist multiplier on the fatigue value of yarn. There is a reduction in fatigue value with an increase in linear density, stretch percentage, and twist multiplier of yarn. The contribution of the number of cyclic loading was found to be more dominating in comparison to other process parameters like linear density, twist multiplier, and stretch percentage on the fatigue value of core-spun yarns. © 2022 Universitat de Barcelona. All rights reserved.

关键词： Fatigue of materials

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Anemia Identification from Blood Smear Images Using Deep Learning: An XAI Approach

Anemia Identification from Blood Smear Images Using Deep Lea...

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Recent Advances in Information Technology for Sustainable Development (ICRAIS), International Conference on

作者： Sneha Sajith Pooja A Theertha Ramesh Parvathi Rajpal Roshna A R Javed Ahammad Asjad Nabeel P Department of Computer Science Govt. College of Engineering Kannur Kerala India IQRAA International Hospital & Research Centre Kozhikode Kerala India

Anemia is a prevalent hematologic disorder characterized by a reduction in the number of red blood cells or a decrease in their oxygen-carrying capacity. Accurate and timely diagnosis of anemia is crucial for effective patient management and improved health outcomes. In this proposed work, we developed a CNN-based model to classify individuals as having symptoms of Iron-deficiency Anemia (IDA) or not, using blood smear images. The CNN model will be trained and fine-tuned using this dataset to maximize the classification accuracy. This helps healthcare professionals to understand the system's decisions and assist in the diagnosis and management of anemia. Despite a relatively limited number of data samples, our proposed work showed promising results by leveraging augmentation techniques. We achieved an accuracy of 0.95, with precision and recall values of 0.9 and 0.89, respectively. The area under the Receiver Operating Characteristic (ROC) curve is reported as 0.98.

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Unveiling Feature Significance: Enhancing Classification Accuracy using Chi-Squared Weighted Feature Selection

Unveiling Feature Significance: Enhancing Classification Acc...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Deepsh Rajput Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer Department of Computer Applications Govt. Engineering College Bikaner India

This study delves into the realm of feature selection and its profound impact on enhancing classification accuracy. Leveraging the renowned Fisher's Iris dataset as a well-established benchmark, our investigation revolves around the application of Chi-squared weights to identify attributes crucial for species classification. The Chi-squared weights are a powerful tool, quantifying the associations between features and species labels. Through this approach, we discern the most pivotal attribute, suggesting its pivotal role in distinguishing iris flower species. By selecting features with elevated Chi-squared weights, we curate a focused feature subset that significantly bolsters classification *** visualization of Chi-squared weights through intuitive bar graphs emerges as an influential tool for gauging feature importance. This graphical representation aids in identifying attributes that possess strong discriminatory capabilities, thereby empowering informed decision-making during feature selection. The study emphasizes the imperative role of feature selection in the domain of machine learning and classification tasks. By incorporating statistical methodologies like the Chi-squared test, we unravel the essence of attribute significance and streamline the process of crafting potent feature subsets. As datasets evolve in complexity, the identification of pivotal attributes becomes indispensable. The proposed methodology provides a valuable approach to address this *** essence, the study advances feature selection techniques by showcasing the potency of Chi-squared weights and their visual representation in enhancing classification outcomes. Our exploration within the Fisher's Iris dataset manifests the broader potential of informed feature selection strategies in elevating the performance of machine learning models.

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Bayesian Optimization with Deep Learning based Crop Type Classification on UAV Imagery

Bayesian Optimization with Deep Learning based Crop Type Cla...

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2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

作者： Chaudhari, Sachin Vasant Polepaka, Sanjeeva Ashraf, Mohd. Shaikhul Swain, Ramakrushna Ananthnath, G.V.S. Bora, Ravi Kumar Sanjivani College of Engineering Department of Electronic and Computer Engineering Ahmednagar Maharashtra Kopargaon India Gokaraju Rangaraju Institue of Engineering and Technology Hyderabad India HKM Govt. Degree College Bandipora Department of Botany Kashmir India Silicon Institute of Technology Department of Computer Science & Engineering Bhubaneswar India KMM Institute of Postgraduate Studies Department of MCA Tirupati India GITAM UNIVERSITY Department of Mathematics Visakhapatnam India

ISBN: (纸本)9781665489621

Precise projections of seasonal agricultural outputs are indispensable for optimizing security of the food. But the gathering of agricultural data via seasonal agricultural studies was frequently not timely sufficient to notify private and public stakeholders regarding crop conditions at the time of growing season. Getting accurate and timely crop approximations are mainly difficult in countries with predominate smallholder farms due to the high diversity of crop types, larger amount of small plots, and intense intercropping. This study emphases on the advancement of Bayesian optimization with deep learning driven crop type classification (BODLD-CTC) technique. The presented BODLD-CTC technique examines the UAV images for the discrimination of crop types. To attain this, the presented BODLD-CTC technique applies Xception model as feature extractor. For classification purposes, the long short term memory (LSTM) model is exploited. At last, the BO algorithm is used to optimally adjust the LSTM hyperparameters and also considerably boost the classification efficiency. To demonstrate the improved outcomes of the BODLD-CTC method, a wide range of simulations were performed. Extensive comparative inspection stated the improvements of the BODLD-CTC method compared to recent models. © 2022 IEEE.

关键词： Crops

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