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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Bhatt, Chandradeep Sagta, Divas Chauhan, Rahul Dhondiyal, Shiv Ashish Sharma, Sanjay Baloni, Dev Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India Sgrr Univeristy Computer Application Department Dehradun India Quantum University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350342338

Like oil in the past, data has become a very important resource in the modern world. But just as oil must be refined in order to be valued, data must also be handled and evaluated in order to yield worthwhile insights. Big Data analytics is a discipline that has developed in response to the expanding demand for efficient data analysis and interpretation. In order to find hidden patterns, connections, and insights, it involves examining huge, complicated datasets utilizing cutting-edge tools and methods. Big Data analytics' importance stems from its capacity to decipher the secrets contained in any dataset and glean insightful information. When appropriately analyzed and addressed, these insights can produce beneficial results. Big Data Analytics is extremely important in the context of consumer behavior. This technology is being used by marketers more frequently to analyze customer behaviors more accurately. Marketers can better understand their target market's preferences, demands, and buying habits by analyzing customer behavior. With the use of this information, marketers can make more informed choices and modify their marketing plans to specifically address customer needs. In the end, this may help to improve firm performance outcomes including boosted sales, enhanced client contentment, and general expansion. The aforementioned study seeks to offer a thorough overview of Big Data technology, including its goal, importance, and potential applications. It also looks into how Big Data technologies affect customer behavior and overall business effectiveness. The goal of this study is to shed light on these elements and the potential advantages and consequences of applying big data analytics across different fields. © 2023 IEEE.

关键词： Big Data Data Analytics Machine Learning RDBMS

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IoT based dual health monitoring device for liquid infusion and urine level measurement of urine bag 3

IoT based dual health monitoring device for liquid infusion ...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumar, Amit Pathak, Pooja Dwivedi, Vijay Kumar Sharma, Dilip Kumar Gla University Upcst Mathura India Gla University Dept. of Computer Engineering and Application Mathura India Gla University Dept. of Mechanical Engineering Mathura India

ISBN: (纸本)9798331528201

Advanced healthcare monitoring devices that improve patient care through real-time data collecting, remote monitoring, and individualized therapy have been developed as a result of the integration of IoT technology in the healthcare industry. Vital indications including heart rate, blood pressure, glucose levels, and oxygen saturation may be continuously monitored thanks to these IoT-enabled gadgets, which include wearable sensors, smart implants, and remote monitoring systems. These devices assist in the early detection of potential health issues, enabling timely intervention. They help reduce hospital readmission rates by monitoring patients remotely. Additionally, they enhance chronic disease management by transmitting real-time data to healthcare specialists. Furthermore, by providing feedback and reminders, IoT-based healthcare devices encourage patient participation and adherence to treatment programs. Despite the many advantages, IoT technology's role in healthcare monitoring is about to change patient outcomes as it develops. The progress in technology over the years has made it possible to use smaller devices, such as smartwatches mobiles, for health monitoring and disease detection. © 2024 IEEE.

关键词： Blood pressure

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Brinjal Leaf Pathology: An Improved Disease Classification in a Federated Learning-CNN Approach 2

Brinjal Leaf Pathology: An Improved Disease Classification i...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Gaur, Poonam Aggarwal, Priyanshi Joshi, Kireet Mehta, Shiva Kukreja, Vinay Cgc Landran Department of Computer Application Mohali India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350360523

A study about brinjal leaf disease diagnosis using federated learning using CNN is presented here. Based on the data from four clients, we can identify four levels of severity for brinjal leaf disease. As a result of the use of federated learning, it is possible to train a CNN model collaboratively while maintaining data privacy and locality, which are essential when dealing with agricultural data. A federated average algorithm was used in our approach to combine local updates of models into a global model. Using this method, the model gains experience from different data sources, thus becoming more generalized and reliable. As evidenced by enhanced model performance among clients, this approach produces tangible outcomes. There were several significant results from the analysis. The macro average of disease diagnosis accuracy through clients is 97.55% in NA-6 and 89.71% in NA-1. As a result of accounting for disease severity classes, the weighted average (Weighted-average) also displays a rising trend. Also, the Micro Average (Micro-ave) shows this trend, running from 89.55% (na-1) to 97.55% (na-6), which represents a general classification success rate. Federation learning can manage brinjal disease severity strata's intricacies and disparities successfully. For a reliable classification of medical conditions, the model must accommodate various inputs, especially among later clients. The study improves agricultural technology by addressing field infections with a practical, scalable, and privacy-preserving method. This study suggests that federated learning and CNN can be used to diagnose crop diseases, revolutionizing the practice. © 2024 IEEE.

关键词： Statistical methods

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Comparative Evaluation of Machine Learning Algorithms for Detecting Credit Card Fraud 8

Comparative Evaluation of Machine Learning Algorithms for De...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prashanth, Banagani Gangadhar, Suddapalli Bhardwaj, Vivek Reddy, Kovvuru Diwakar Reddy, Puli Shiva Mehra, Manik Lovely Professional University Department of Computer Science and Engineering Phagwara India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India Lovely Professional University School of Computer Application Phagwara India

ISBN: (纸本)9798350396638

Detecting credit card fraud is an extremely important issue in the financial sector, as it results in significant financial losses and negatively impacts customer trust. The main motive of CCFD is to develop methods to identify fraudulent transactions accurately and efficiently. This research typically involves the use of statistical and machine-learning algorithms to analyze large amounts of transaction data. Utilizing machine learning is a potential solution for addressing credit card fraud. Studies suggest that machine learning techniques can help overcome the challenges of identifying fraudulent transactions with high detection rates, both directly and indirectly. While supervised ML algorithm and unsupervised ML algorithms have been proposed, the limitations of each approach underscore the need for hybrid methods. From the results, it is evident that Logistic Regression has the highest accuracy of 94.86%, highest recall value of 98.97% and highest F1-score of 95.09%. © 2023 IEEE.

关键词： Feature extraction

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Unsupervised Anomaly Detection Based on CNN-VAE with Spectral Residual for KPIs 24

Unsupervised Anomaly Detection Based on CNN-VAE with Spectra...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Gongliang Wen, Zepeng Xie, Xin Institute of Computer Application Technology China Academy of Engineering Physics College of Computer Science Sichuan University Mianyang China Institute of Computer Application Technology China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350319934

Current large-scale applications, such as trading systems, blockchain, social software, etc, are increasingly adopting microservice architecture, which bring challenges to manual operation and maintenance, intrusion detection. In both operations and intrusion detection, there are a common characteristic that service metrics and network traffic are normal for most of the time, but anomaly data is more important. In this paper, we propose an unsupervised anomaly detection algorithm based on convolutional neural network with Spectral Residual, which is verified experimentally and has potential application capability with 19.2% f1-score improvement compared to the Variational AutoEncoder. © 2022 IEEE.

关键词： Intrusion detection

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Brain Tumor Radiogenomic Classification using multilevel contrastive information fusion

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Procedia computer Science 2025年 259卷 260-268页

作者： Asmita Praveen Mittal Department of Computer Engineering Application GLA University Mathura Uttar Pradesh 281406 India

Brain tumors present significant challenges in diagnosis and treatment due to their complex and diverse nature. Radiogenomic analysis, which combines genomic and radiological data, has become important for advancing brain tumor research, with deep learning methods, particularly fusion-based approaches, making notable progress. However, current approaches face challenges in capturing both sequential and spatial features, as well as selecting effective fusion techniques. The research introduces a multi-fusion framework that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers using contrastive learning to enhance feature representation. This approach processes medical images through a neural network alongside a transformer backbone, merging outputs at various stages to improve dark knowledge differentiation between classes. While CNNs extract spatial features, transformers focus on patch-specific information, collectively boosting the model’s discriminative power. The method demonstrates a 2-3% performance improvement over state-of-the-art models, with significant advancements in glioblastoma diagnosis and treatment. An ablation study further confirms that each component positively contributes to the classification task.

关键词： Brain tumor Radiogenomic Classification Deep Learning Image Classification

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI 12

Analytical Evaluation of Web Performance Testing Tools: Apac...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Tiwari, Vatsya Upadhyay, Sachin Goswami, Jayati Krishna Agrawal, Sanjiv Bsa College of Engineering & Technology Department of Computer Science & Applications Mathura India GLA Univ. Deparmtnent of Computer Engineering & Application Mathura India G L Bajaj Group of Institutions Computer Sciecnce & Engineering Mathura India

ISBN: (纸本)9781665462617

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed. © 2023 IEEE.

关键词： Load testing

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Next Generation Digital Health Monitoring System Using Artificial Neural Network 5

Next Generation Digital Health Monitoring System Using Artif...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Kadayat, Yunika Sharma, Sachin Painuly, Sakshi Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University Department of Computer Application Dehradun India

ISBN: (纸本)9798350351378

By empowering early identification and provoke reaction within the occasion of health irregularities, another era digital health monitoring systems are basic to guaranteeing people's prosperity. ANNs, or artificial neural networks, have become successful devices for making exact and successful health monitoring systems. This ponder offers a careful examination of how ANNs are utilized in health monitoring systems. It covers the standards of ANNs, considers how they fit into systems for health monitoring, gives case illustrations, and talks around the obstacles and up and coming advancements in this region. © 2024 IEEE.

关键词： Neural networks

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Research on Fast Beam Alignment Technology at Target Area Based on Swarm Collaboration Control Model

Research on Fast Beam Alignment Technology at Target Area Ba...

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2022 International Conference on computer engineering and Artificial Intelligence, ICCEAI 2022

作者： Xie, Xingquan Luo, Jun Cao, Zhike Zhou, Xiaowei Yun, Tianyou Li, Li Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

In this paper, in order to reduce the total time for aligning all laser beam of an large-scale laser facility at target area, a fast convergence control mode of beam swarm alignment based on the swarm collaboration control mode was constructed, and a beam alignment fast convergence algorithm was designed on the basis of this model. According to the data which was collected at the facility site and running data in the 8-bundle serial alignment mode, the algorithm was simulated for 20 shots. The simulation results show that the algorithm can greatly reduce the average alignment time of 8 beams, from 233.7s to 162.2s, and the reduction rate is greater than 30%. The algorithm proposed in this paper has a certain reference for the design and engineering application of large-scale laser facility. © 2022 IEEE.

关键词： Alignment

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