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The Large Action Model: Pioneering the Next Generation of Web and App Engagement

The Large Action Model: Pioneering the Next Generation of We...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Garima Singhal Aniket Singh School of Management Noida Institue of Engineering and Technology Greater Noida India Dept. of Computer Science Delhi Technical Campus Greater Noida India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

In the digital realm, constant attempts are made to enhance system performance and user engagement. The Large Action Model's (LAM's), a whole architecture with the power to fundamentally alter the online and application interface environments, is at the forefront of innovation. The intricate structure of LAM is examined in this work, highlighting its formidable mathematical building blocks and its potential to revolutionize digital interactions. LAM's blends theoretical brilliance with practicality via its innovative approach to modeling user behavior and system dynamics. The research thoroughly dissects the fundamental components of LAM's, emphasizing how crucial it is for optimizing backend operations. The study demonstrates how LAM's may significantly increase operational effectiveness and user engagement by evaluating its impact on important performance metrics via a rigorous analytical process. The results of the research demonstrate the revolutionary nature of LAM's and its role as the engine of an unprecedented era of the digital connection. The research subtly casts doubt on the broader ramifications of LAM's, suggesting that it may change the laws governing operational resilience and user happiness in the digital age.

关键词： Measurement Technological innovation System dynamics System performance Market research Mathematical models Reliability

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Securing IoT Networks in Healthcare for Enhanced Privacy in Wearable Patient Monitoring Devices

Securing IoT Networks in Healthcare for Enhanced Privacy in ...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Tiwari, Vibha Jharbade, Nitin Chourasiya, Prashant Supraja, B. Wani, Priyanka Sujit Maurya, Rashmi Madhav Institute of Technology and Science Department of Centre for Artificial intelligence M. P. Gwalior India GGITS Dept of Computer Science & Business System M.P. Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P. Bhopal462044 India Guru Nanak Institutions Technical Campus Department of CSE - AIML Hyderabad India Dr. D. Y. Patil Institute of Technology Dept. of E&TC Maharastra Pune India

ISBN: (纸本)9798331539948

The project aims to make smart patient monitoring devices more private. This is crucial to healthcare IoT network security. Strong security is needed to protect patient data in a growing digital healthcare system. Our research develops a whole security system with sophisticated encryption, user-centric identity, blockchain integration, and deep learning to detect unusual activity. As mentioned before, sophisticated patient monitoring gadgets in contemporary healthcare have increased security vulnerabilities. A powerful and adaptable security system is needed to secure patient data;hence, a multidimensional approach is studied. This study's approaches include experimental design, dataset preparation, and measure choice. The security framework uses cutting-edge cryptography, including ECC, homomorphic encryption, and blockchain. Also crucial are recognizing users based on biological features and spotting abnormalities using deep learning and LSTM networks. Mixed methods mitigate all security issues. The findings reveal that the recommended strategy boosts encryption to 256 bits. Since authentication accuracy has reached 98%, user-centered authentication approaches work. Cryptographic processes employ resources effectively, and anomaly detection rates reflect how successfully the approach detects unusual device behavior. Elliptic Curve Cryptography (ECC), which may give 256-bit encryption, is stronger than symmetric encryption (128 bits). User-centric authentication was 98% accurate, outperforming public key infrastructure (95%). With a 92% success rate, LSTM networks can predict abnormal activity in connected devices. The proposed approach performs cryptographic operations fast, with 120 operations per unit time. In conclusion, this research introduces a healthcare IoT security solution. The combined encryption, identity, blockchain, and deep learning technology is secure. The recommended solution protects patient data on personal tracking devices with greater encrypt

关键词： Differential privacy

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A Novel Deep Learning Based Approach for Detecting and Localization of Pulmonary Cancer

A Novel Deep Learning Based Approach for Detecting and Local...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Amit Kumar Mishra Rahul Sharma Jagendra Singh Prabhishek Singh Manoi Diwakar Mohit Tiwari Department of Computer Science & Engineering Graphic Era Hill University Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science & Engineering Bharati Vidyapeeth's College of Engineering Delhi India

In the ever-evolving field of scientific diagnostics, the early diagnosis of pulmonary most cancers continues a key undertaking. This observation proposed a unique deep learning-primarily based approach, in particular using Generative Adversarial Networks (GANs), intending to modernise the identification and localization of pulmonary malignancies through scientific imaging. Our models, trained using a varied dataset, demonstrated a promising accuracy fee of 70% within the sample set, suggesting its ability to adept.y differentiate between malignant and non-malignant instances in scientific images. While the conclusions suggest a significant growth in lung cancer detection, also they highlight locations demanding in addition refinement. The balance of technological prowess and scientific significance, as reflected through criteria like sensitivity and specificity, remains a focus topic for future projects. The outcomes of this research are substantial. Beyond the on the spot discoveries, the take a look at emphasizes the transformational possibility of incorporating sophisticated AI approaches into healthcare. As the scientific network grapples with the difficulties of early cancer identification, gear like the one displayed in this study ought to usher in a new age in diagnostics-marked by accuracy, efficiency, and patient-centricity. In conclusion, this have a look at now not simplest adds a fresh diagnostic tool to the sector but moreover sets the way for future innovations within the confluence of AI and healthcare.

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Real-Time Web Application to Classify Diabetic Foot Ulcer 14

Real-Time Web Application to Classify Diabetic Foot Ulcer

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nandi, Souhrid Anurag, Apoorva Mayya, Veena Jeganathan, Jayakumar Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science & Engineering Technology Karnataka Manipal India Manipal Academy of Higher Education Manipal Institute of Technology Department of Information & Communication Technology Karnataka Manipal India Manipal Academy of Higher Education Kasturba Medical College Dept. of Medicine Karnataka Mangalore India

ISBN: (纸本)9798350335095

Diabetic foot ulcers are one of the major complications of type 2 diabetes and can result in gangrene and amputation if left untreated. Early detection and treatment are essential for reducing the risk of amputation. Imaging, such as digital photography or imaging scans, can be a valuable tool in detecting and monitoring diabetic foot ulcers, documenting their size and location, identifying underlying issues, and evaluating treatment effectiveness. This paper utilises a two-dimensional image as its input and applies an ensemble of neural networks to analyse the images. The system is capable of performing image classification into four distinct states, infection, ischaemia, both, and none. © 2023 IEEE.

关键词： Convolutional neural networks

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Remote Monitoring for Early Detection of Diabetic Foot Ulcers-An Effective Analysis

Remote Monitoring for Early Detection of Diabetic Foot Ulcer...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： N. Gomathi A. Tejashwini S. Mahi Dept. of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Diabetes is a prevalent and serious health concern with significant economic burdens. Diabetic foot ulcers (DFUs) are a major complication, affecting 15-34% of diabetic individuals. These slow-healing wounds on the foot bottom significantly impact patients' well-being. This paper explores the use of a classification algorithm, specifically Decision Tree (DT) analysis, for the sensitive analysis of temperature data. DTs aim to predict the correct labels for new data points. In this context, the model is trained on a comprehensive dataset, validated, and tested before being used to predict the presence of DFUs in unseen patients based on temperature measurements. By analyzing temperature differences between similar regions and identifying elevations above a threshold linked to inflammation, the DT model can potentially aid in early DFU detection.

关键词： Temperature measurement Temperature sensors Temperature distribution Mental health Medical services Wounds Predictive models

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Review Of The Recent Methods For Classification Of Colorectal Polyps Using computer Aided Diagnosis

Review Of The Recent Methods For Classification Of Colorecta...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Akshaya Nair A L Sangal Dept. Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar India

Colorectal cancer is one of the prevalent forms of fatal cancer. The presence of tumors inside the colon is identified through colonoscopy which helps to determine whether the polyp is benign or malignant. Prediction of the polyp histology during colonoscopy itself will help to reduce the number of unnecessary biopsies due to false diagnosis. Hence, accurate classification of the polyp is critical for effective treatment and improved patient outcomes. Over the years, lots of work has been done to improve the performance of colorectal cancer classification systems using computer aided diagnosis. This paper presents the various approaches used for colorectal cancer classification while also addressing the existing limitations associated with different methodologies. Although substantial progress has been made in this field in the past few years, several important obstacles must still be overcome to achieve an accurate and reliable classification of polyps. This article will also help academicians and practitioners in this field to familiarize with state-of-the-art techniques.

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Design and Construction of an Arbitrary Pulse Compressive Amplifier

Design and Construction of an Arbitrary Pulse Compressive Am...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Cody Goins Aaron Harmon Victor Khilkevich Daryl Beetner Dept. of Electrical and Computer Engineering - Electromagnetic Compatibility Laboratory Missouri University of Science and Technology Rolla MO USA

Compressive pulse amplifiers are a class of amplifiers that convert long low amplitude signals into very broadband pulses of high amplitude, yielding a very high instantaneous peak power output pulse. However, in the realm of electronic immunity and susceptibility testing, very broadband short pulses are not always desired. This work presents a design for a compressive amplifier that is aimed at creating arbitrary pulsed signals of varying bandwidths. Limitations of the achievable gain and methods used are discussed.

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Neural Network Based Fault Location in Power Distribution System

Neural Network Based Fault Location in Power Distribution Sy...

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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Viresh Patel Soumyajit Ghosh Saikat Chakrabarti Ankush Sharma Sanjeev Pannala Dept. of Electrical Engineering Indian Institute of Technology Kanpur Uttar Pradesh India Dept. of Electrical and Computer Engineering University of New Brunswick Fredericton NB Canada School of Electrical Engineering and Computer Science Washington State University Pullman WA USA

Locating the fault in the power distribution system is a tedious process that involves manually searching along the line. While some methods rely on complete system parameters or centralized data processing, it has become difficult due to the bidirectional power flow in the active distribution system. This paper proposes a framework consisting of three steps: detecting, classifying, and locating the fault in the active distribution system. The proposed method reduces the search area by using the sending-end sample value of the line current. It can be implemented in a distributed form without knowledge of system parameters using pole-mounted data recording meters and relaying the approximate fault location and type to the control center. In the first step, the fault is detected using an isolation forest. Then, the fault is classified by the support vector machine, and finally, the fault is located using an adaptive weight convolutional neural network (CNN). The CNN weights are modified according to fault type information that uses time-frequency information extracted by a continuous wavelet transform (CWT). The proposed framework is tested using an IEEE 13-node test feeder with a solar photovoltaic system. Different training parameters for the CNN are also tested to analyze the proposed framework

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Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer

Wetland Segmentation Method for UAV Multispectral Remote Sen...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Pakezhamu Nuradili Ji Zhou Farid Melgani School of Resources and Environment University of Electronic Science and Technology Chengdu China Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this study, an end-to-end semantic segmentation method (ConvSegFormer) is proposed by utilizing the multispectral imaging capability of UAVs for images containing multispectral bands, with a special focus on thermal infrared bands. Experimental results show that the use of multispectral images, especially thermal infrared bands, achieves higher segmentation accuracy through spectral information. In addition, the end-to-end deep learning semantic segmentation method can directly learn the complex mapping relationship between image pixels and semantic categories without step-by-step feature extraction and classification, which is more direct and efficient. Finally, the maximum values of Mean Pixel Accuracy (MPA) and Mean Intersection Over Union (MIOU) are 90.35% and 73.87%. In the segmentation task of the wetland area, the maximum values of PA and IOU reached 95.42% and 90.46%. This indicates that the method is effective and feasible in automatically extracting the segmentation of wetlands and other land types.

关键词： Deep learning Accuracy Satellites Multispectral imaging Semantic segmentation Semantics Autonomous aerial vehicles

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Pelican optimized extreme learning machine based prognosis of heart failure using textural patterns in CMR images

Pelican optimized extreme learning machine based prognosis o...

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technology & engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： M Muthulakshmi K Ashwini R Jansi Batchu Bhanu Sai Mani Kiran Nimmagadda Greeshma Pulimi Keerthi Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Dept. of Electronics And Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Computer and Communication Engineering Amrita School Of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350384659

ISBN: (纸本)9798350384666

One of the abnormal cardiovascular conditions with the greatest rate of increase is heart failure (HF). There is a lot of correlation in the exterior symptoms observed that are typically ignored, particularly when there is progression from one stage to another. Planning an efficient treatment requires grouping patients into different phenotypic categories. In CMR imaging, these conditions show damaged heart muscles. Understanding the classification performance of ELM aided by optimal texture features is the main aim of the proposed work. In this study, Krawtchouk moment and the co-occurrence of neighboring sparse local ternary pattern descriptors are used to extract texture and anatomical information of LV. However, an effective feature selection strategy is required to handle the high dimensionality of the feature vector. The primary benefit of the Pelican optimization algorithm (POA), which takes inspiration from nature, is its capacity to carry out both global and local searches effectively. This paper suggests usage of multi-objective POA to optimize the proportion of extracted features and improve the performance of the ELM classifier. The people with various HF stages have been diagnosed with the maximum accuracy of 95.7 % as a result of this inclusion. The enhanced traits have distinguished moderate and severe HF patients from control participants with significant performance. The proposed work further shows the impact of tissue characterization from CMR image and optimized features-based classification on the HF stage detection.

关键词： Heart Magnetic resonance imaging Myocardium Feature extraction Prediction algorithms Vectors Planning

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