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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Sarkar, Paramita Srivastava, Ashutosh bhardwaj, Arpit Verma, madhushi B.M.S. Institute of Technology and Management Department of ECE Bangalore560064 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350364590

The Solar Cell Capacitance Simulator (SCAPS-1D) is used to conduct extensive research on the design of perovskite solar cells based on hybrid metal halide (CH3NH3SnI3), with the goal of increasing their energy conversion capability. The proposed device (ITO/SnO2/CdS/CH3NH3SnI3/CuSCN/Au) performance has been explored by optimizing the design variables like temperature (280K to 330K) and absorber layer thickness (750nm to 1300nm). moreover, the impact of cadmium sulfide, 'CdS' as buffer layer is also studied. An analysis has been conducted in order to ascertain the best composition of the device's layers for optimal performance. These layers include the CH3NH3SnI3as absorber/active layer, copper (I) thiocyanate (CuSCN) as hole transport layer (HTL), tin dioxide (SnO2) for electron transport (ETL), and CdS for active buffering. At optimized temperature of 280K and thickness of 950nm, the open circuit voltage is 1.05V, fill factor is 87.44%, short circuit current density is 33.89mA/cm2 and efficiency is 31.06%. © 2024 IEEE.

关键词： Tin dioxide

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Enhanced Framework for Authentic and Anonymous Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, m. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. by intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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Enhanced Predictive model to Diagnose Coronary Heart Disease Using Decision Tree 2

Enhanced Predictive Model to Diagnose Coronary Heart Disease...

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

作者： Joshi, Sujata Dixit, Ananya Nair, mydhili K. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India B.M.S. Institute of Technology and Management Department of Information Science and Engineering Bangalore India School of Computer Science Rv University Bangalore India

ISBN: (纸本)9798350373110

As per WHO reports, Heart diseases or Cardiovascular Diseases (CVDs) rank as the leading cause of death worldwide. Approximately 17.7 million individuals succumbed to CVDs in 2015, accounting for 31% of all global fatalities. Among these fatalities, an estimated 7.4 million were attributed to coronary heart disease, one of the prevailing forms of heart disease.. According to the Global burden of Disease study, nearly quarters (24.8%) of all deaths in India are due to CVD. Hence there is a need for careful and periodic methods of examining heart related diseases. The majority of cardiovascular diseases found in India are coronary heart diseases (CHD) The main contribution of this work is to develop efficient predictive models for Coronary heart diseases using decision trees. In this work, the predictive models are developed for diagnosis of heart disease using the cardiovascular disease dataset from UCI repositories with the objective of improving the accuracy. We have also obtained echocardiograph data obtained from m. S. Ramaiah hospital and used in this work. It is found that the data used in this work is heterogeneous in nature and the decision trees work well on heterogeneous data and also they are expressive and interpretative, we have opted to use decision trees. The predictive model namely Enhanced-Pred-model CVD-DATA-DT is developed using both the datasets. It is found that the model shows marginal accuracy increase from 77% in baseline model to 80% by the proposed model. The proposed method has better performance in terms of accuracy as well as error rate. The models are also tested on the echocardiograph data for prediction of LVH event and has shown improvement in the performance. © 2024 IEEE.

关键词： Prediction models

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End to End model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks 2

End to End Model to Reduce the Inference, Jamming, and to In...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Dhananjaya, Shashank Narender, m. Sunitha, R. The National Institute of Engineering Department of Information Science and Engineering Mysore India The National Institute of Engineering Department of Computer Science and Engineering Mysore India B. N. M. Institute of Technology Dept. of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350306415

Wireless technology has advanced dramatically in recent years. multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has been challenged by the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization by allowing users to access unused spectrum bands on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all become key issues. Proposing a unique approach to address the interference problem produced by secondary nodes that have been hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ability to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. many researchers have been working on reducing inference and the effect of jamming in the network. but still it is not resulted with the proper solution for the jamming problem in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security problem to increase the trust in the cognitive radio network. The secondary users must be able to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance by reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network. © 2024 IEEE.

关键词： Cognitive Radio Network Inference Jamming Q-Learning Software Defined Radio Trust

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Synchronization and implementation of real-time traffic signal optimization regulator

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multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, m. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control System (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer Science+business media, LLC, part of Springer Nature 2024.

关键词： Travel time

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Cardiovascular Predictive Analysis with machine Learning Fusion 6th

Cardiovascular Predictive Analysis with Machine Learning Fus...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Ahire, Aaditya mehta, Dimple Amith Shekhar, C. Dharrao, Deepak bongale, Anupkumar m. Maharashtra Pune412115 India Department of Computer Science and Engineering B N M Institute of Technology Bengaluru India Maharashtra Pune412115 India

ISBN: (纸本)9789819734412

Predicting heart strokes is a critical task in the field of healthcare, and it is further complicated by the multifaceted nature of cardiovascular health. In recent years, machine learning algorithms have emerged as valuable tools for forecasting heart stroke occurrences. Among these methods, ensemble models have shown significant promise by amalgamating the predictions of multiple base models. To overcome the existing limitations and enhance the accuracy of heart stroke prediction, the research introduces a novel ensemble model, combining stacked long short-term memory (LSTm) and XGboost, which leverages the strengths of both these algorithms. The proposed model is compared with alternative methods, including individual stacked LSTm and XGboost models, to assess its effectiveness in predicting heart strokes. The ensemble model is trained and evaluated using extensive historical health data. Evaluation metrics such as accuracy, precision, F1 score, and recall are employed to assess the performance of the forecasting model. The results of this study reveal that the ensemble stacked LSTm-XGboost model outperforms individual stacked LSTm and XGboost models, demonstrating superior predictive accuracy in capturing heart stroke occurrences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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A machine Learning And Iot based Contactless Entry System 5

A Machine Learning And Iot Based Contactless Entry System

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5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Jain, Vishal Saini, Nidhi bera, Some Jana, Keya B. M. Institute of Engineering and Technology Department of Electronics and Communication Engineering Sonepat India B.M. Institute of Engineering and Technology Department of Computer Science & Engg Sonepat India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

The COVID-19 pandemic has intensely impacted humanities globally. This scenario has laid down many protocols and procedures to mitigate the risk and thus ensure the safety of individuals. The pandemic has augmented the demand of contactless mechanism especially in the public *** paper presents novel machine learning and internet of things based solution for contactless entry to premises following mandatory checks at the entry as per COVID-19 protocols. The proposed model senses the body temperature and detects the face mask of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body temperature is within prescribed limits and the face mask is properly put *** current work uses a machine learning model for detection of the face mask which uses the real time image during screening;the algorithm is trained using the data sets with and without mask. The temperature screening is carried out with temperature sensor connected to the Arduino processor.A prototype model using Arduino is prepared based on the inputs received from the temperature sensor and the machine learning Algorithm. The gate shall open for entry if a person has normal body temperature and wearing a proper face mask, else, will be restricted through a custom alert. Also a track of the number of persons entering is monitored and sent to a web portal on real time basis to account for *** model ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.

关键词： COVID-19

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Heart Stroke Predictive Analysis with machine Learning Ensembling 6th

Heart Stroke Predictive Analysis with Machine Learning Ensem...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

ISBN: (纸本)9789819778614

Predicting heart strokes is a critical task in the field of health care, and it is further complicated by the multifaceted nature of cardiovascular health. In recent years, machine learning algorithms have emerged as valuable tools for forecasting heart stroke occurrences. Among these methods, ensemble models have shown significant promise by amalgamating the predictions of multiple base models. To overcome the existing limitations and enhance the accuracy of heart stroke prediction, the research introduces a novel ensemble model, combining Stacked Long Short-Term memory (LSTm) and XGboost, which leverages the strengths of both these algorithms. The proposed model is compared with alternative methods, including individual Stacked LSTm and XGboost models, to assess its effectiveness in predicting heart strokes. The ensemble model is trained and evaluated using extensive historical health data. Evaluation metrics such as accuracy, precision, F1-score, and recall are employed to assess the performance of the forecasting model. The results of this study reveal that the ensemble Stacked LSTm-XGboost model outperforms individual Stacked LSTm and XGboost models, demonstrating superior predictive accuracy in capturing heart stroke occurrences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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Sustainable Power Generation from Waste: Reducing Landfill Waste and meeting Growing Energy Demands 3

Sustainable Power Generation from Waste: Reducing Landfill W...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sheikdavood, K. Asokan, R. Thangaraju, Dineshkumar Vedhashree, S.K. Lakshmi, R. Arumugam, Arul R. M.Kumarasamy College of Engineering Karur India Kongunadu College of Engineering and Technology Trichy India Karpagam Institute of Technology Coimbatore India V.S.B Engineering College Karur India Annapoorana Engineering College Salem India

ISBN: (纸本)9798331532420

The increasing popularity of electric vehicles and other electronic devices is causing a gradual increase in the demand for electricity in daily life. Electricity has been produced through methods like thermal power plants and hydroelectric power plants, both of which depend on different fuel sources. With the help of a light source, we will store waste and use it to generate electricity through incineration, reducing the amount of land that ends up in landfills and simplifying waste management. In addition to reducing pollution through incineration, it can meet the growing demand for electricity by harnessing the energy potential of stored waste. This new invention provides a sustainable solution by creating electricity from waste materials and reducing the environmental impact of disposing of waste. © 2024 IEEE.

关键词： Land fill

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Deep Learning based Abstractive text Summarization: A Survey 6

Deep Learning based Abstractive text Summarization: A Survey

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6th Parul International Conference on engineering and technology, PICET 2024

作者： Kiran, G. Uday Gandi, Ramakrishna Lavanya, m. Desale, Girish bhagwant Rao, Ch. Upendar Reddy, b. Veerasekhar B V Raju Institute of Technology Narsapur India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science India Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science IT JET'S Z. B. Patil College M.S. Dhule India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798350369748

The explosion in the amount of textual data in the last several years has created an abundant source of resources for data extraction and analysis. It is necessary to summarize this data in order to access relevant information in a fair amount of time. Here we take a look at some of the more current methods for summarizing abstractive texts with deep learning models. A hotspot for research in the field of Natural Language Processing (NLP) is summarization, the process of condensing a document without losing any of its significance. You can classify summarization methods as either "extractive" or "abstractive" depending on whether they use natural language processing to construct new phrases or keep the exact ones from the source text. Extensive research has been conducted on extractive summarization, and the issue has now attained maturity. At this point, the focus of the study is on providing an abstract summary. Abstractive Text Summarization (ATS) is a tough and complicated operation because of the inherent complexity of the natural language content. Researchers can get a thorough overview of Deep Learning based ATS in this work. This paper provides a comprehensive survey of deep learning models employed for abstractive summarization, delving into their architectures and methodologies. Furthermore, it scrutinizes prevalent datasets utilized for training and evaluation purposes, offering insights into their characteristics and suitability for benchmarking. The study evaluates the performance of various abstractive summarization methods on these datasets, shedding light on their effectiveness and limitations. Lastly, emerging themes in the field are discussed, along with unresolved challenges, aiming to foster deeper understanding and advancement in DL-based abstractive text summarization among researchers. © 2024 IEEE.

关键词： Contrastive Learning

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