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2023 International Conference on data science and Network Security, ICDSNS 2023

作者： Kataria, Aman Puri, Vikram Pareek, Piyush Kumar Rani, Sita Amity Institute of Defence Technology Amity University Noida Campus Sector-125 Noida India Duy Tan University Center of Visualization and Simulation Da Nang Viet Nam Cell Nitte Meenakshi Institute of Technology Dept. of Artificial Intelligence and Machine Learning and Ipr Bengaluru India Guru Nanak Dev Engineering College Department of Computer Science and Engineering Ludhiana India

ISBN: (纸本)9798350301595

Wearable body sensors have recently gained popularity because they monitor user behaviors and vital indicators. These sensors are designed to monitor various physiological factors, such as motion, temperature, and heart rate, among others. Making sense of the data gathered by these sensors requires separating the dependent and independent features. The dependent parameters are those that body-worn sensors can read in this scenario. Examples of such elements include blood oxygen levels, heart rate, temperature, mobility, and airflow. These sensors capture data about a person's activity and health. The independent parameters in wearable body sensors are related to the dependent parameters, whereas the dependent parameters are those that the sensors can measure directly. Such characteristics include age, sex, weight, and environmental influences. For example, humidity and airflow might impair a temperature sensor's accuracy. The user's age or level of physical fitness may also influence the accuracy of a heart rate monitor. Activity monitors are used in this study to capture the actions of various people. Static and dynamic properties are derived from the activity monitor recording. The performance evaluations of the eight distinct machine-learning models that were created using the retrieved features are discussed in this paper. The Grid Search method is used to enhance the performance of the XGB model. © 2023 IEEE.

关键词： Heart

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Smart Locker 2.0: Leveraging IoT and machine learning for Secure, User-Friendly Public Storage

Smart Locker 2.0: Leveraging IoT and Machine Learning for Se...

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Artificial Intelligence and data Engineering (AIDE), International Conference on

作者： Chandrappa S Prithviraj Aditya Pai H Rajesh I S Manjunath Sargur Krishnamurthy R Sneha Shetty R Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte India Department of CSE MIT Art Design and Technology University Pune India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India JP Morgan & Chase Co Houston USA Dept. of Artificial Intelligence and Machine Learning Nitte (Deemed to be University) NMAM Institute of Technology Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Smart locker systems are a revolutionary technology, combining advanced automation and connectivity to address increasingly demanding requirements for secure, efficient, and convenient storage solutions in numerous scenarios. These intelligent lockers use the latest technologies, such as the Internet of Things (IoT) and mobile applications, to revolutionize the way people store their belongings. Existing Smart Locker System lacks measurable results. To sustain its claims, we include key findings. For security, the system utilizes RFID authentication and biometric security, which reduces unauthorized access by 98% and blocks spoofing attacks. For convenience, a study with 50 users found 92% satisfaction, with 87% preferring it over traditional lockers due to faster access, averaging 3 seconds. For environmental benefits, the system cut power use by using sleep mechanism and saved paper usage by switching to cloud logs. Real-World tests achieved 98% user satisfaction with the university system proving its efficiency, security, and sustainability. This system expounds on multifaceted benefits and functionalities in smart lockers. First, it covers how these lockers provide better security compared to old lock-and-key systems. Using access controls reduces the risk of theft and illegal access to one's valuables and sensitive belongings by a great degree. It has an easy reservation, access, and management of personal lockers via a remote means; this proves to be seamless without needing any physical keys and contactless interactions, ideal for modern spaces considering hygiene and efficiency is top priority. Moreover, it goes on to analyze the flexibility of smart locker systems. From corporate offices to co-working spaces, universities, shopping malls, and public transportation hubs, these lockers cater to diverse user needs, creating flexible and scalable solutions adaptable to a variety of environments.

关键词： Biometrics Cloud computing Switches machine learning Mobile applications Internet of Things Sustainable development Secure storage Public transportation Radiofrequency identification

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Harshitha Chilupuri G. Ramesh J. Praveen Venkataramaiah Gude Dept. of Artificial Intelligence and Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electrical and Electronics Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India GP Technologies LLC U.S.A.

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The paper presents the development of a data science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods.

关键词： Visualization Image transformation Generative AI data science Libraries Performance analysis Indexes Advertising Optimization Python

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Exploring Advanced Techniques in Computer and Network Forensics for Enhanced Cybersecurity

Exploring Advanced Techniques in Computer and Network Forens...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Nagpal, Amandeep Kumar, S Vinod Reddy, Uma Radha, K. Kumar, Ashwani Abdulla, Namaat R. Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India St. Martin's Engineering College Dept. of It Telangana Secunderabad India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Because cybersecurity is continually changing, it's crucial to have effective, adaptable defenses. This paper demonstrates a novel computer security method. This is done via blockchain technology, enhanced cryptography, threat intelligence integration, machine learning-based strangeness detection, and behavioral analysis. After carefully comparing our solution to others, we feel it is the best way to secure computer and network systems. The machine learning-Based Anomaly Identification approach accurately identifies anomalies using complex mathematical calculations and repetitive procedures to adjust parameters. Blockchain technology unites odd data into a decentralized ledger using encryption for forensics efficiency. Homomorphic encryption and quantum-resistant signatures protect the program against new attacks. The proposed method exhibits superior performance across various metrics, achieving a detection accuracy of 98.5% with a low false positive rate of 1.0%. Visualizations elucidate trade-offs, trends, and classification metrics. This cybersecurity framework offers a promising solution for addressing the dynamic challenges posed by a wide range of cybersecurity threats. Its adaptability, versatility, and collective efficacy position it as a noteworthy advancement in the pursuit of securing digital environments from evolving and sophisticated cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Brain Cancer Tumor Detection by U-Net Deep learning Algorithm from MRI Images 12

Brain Cancer Tumor Detection by U-Net Deep Learning Algorith...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Das, Utpal Chandra Benjapolakul, Watit Gupta, Manoj Vitoonpong, Timporn Suanpang, Pannee Somthawinpongsai, Chanyanan Butdisuwan, Sujin Nanthaamornphong, Aziz Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Dept. of Rehabilitation Medicine King Chulalongkorn Memorial Hospital Dept. of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Department of Information Technology Faculty of Science and Technology Suan Dusit University Bangkok Thailand Shinawatra University Pathum Thani12160 Thailand College of Computing Prince of Songkla University Phuket Campus Phuket Thailand

ISBN: (纸本)9798350387315

This research looks at the genomic subtypes of low-grade glioma tumors and their shape characteristics by deep learning magnetic resonance image (MRI) segmentation. We analyzed preoperative imaging and genetic data from 110 patients with low-grade glioma from the Cancer Genome Atlas. Three shape features were recovered to quantify the two- and three-dimensional aspects of the malignancies. Based on gene expression, DNA copy number, IDH mutation, 1p/19q co-deletion, DNA methylation, and microRNA, previously identified clusters were found in genomic data. We used the exact trait test to investigate the connection between chromosomal clusters and imaging traits. Our findings show a significant correlation between the margin fluctuation-bounding ellipsoid volume ratio and the RNA Seq clusters. Furthermore, a correlation was discovered between RNA-seq clusters and angular standard deviation. The U-net deep learning algorithm demonstrated a test accuracy of 94\% and a mean Dice coefficient of 90\%. These findings suggest that tumor shape characteristics derived from MRI can be projected through genomic subtypes in lower-grade gliomas. © 2024 IEEE.

关键词： Gene expression

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics

arXiv

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arXiv 2024年

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model’s ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024, CC BY-SA.

关键词： State space methods

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PsychBERT: A Mental Health Language Model for Social Media Mental Health Behavioral Analysis

PsychBERT: A Mental Health Language Model for Social Media M...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Vajre, Vedant Naylor, Mitch Kamath, Uday Shehu, Amarda Stone Bridge High School Ashburn United States Applied Machine Learning Smarsh Nashville United States George Mason University Dept. of Computer Science Fairfax United States

ISBN: (纸本)9781665401265

Mental health behaviors are now recognized as primary factors contributing to suicide. This paper puts forth a novel mental health language model to address mental health and makes several contributions. First, it proposes a taxonomy and puts forth a comprehensive dataset of social media text. Second, it proposes a two-stage framework, first discriminating text relevant to mental health from non-relevant text and then carrying out multi-class classification for detection of mental health behaviors. Third, it proposes a novel mental health language model, PsychBERT, which is pretrained on a large corpus of biomedical literature on mental health and social media data. Fourth, the framework additionally incorporates components that enhance its explainability. Our evaluation shows that the proposed framework is outperforms state-of-the-art methods and is interpretable. Pre-trained PsychBERT is made publicly available for the community at https://***/mnaylor/psychbert-cased. © 2021 IEEE.

关键词： Health

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An end-to-end interpolated Automatic speech recognition system with punctuated transcripts for the Hindi language 6

An end-to-end interpolated Automatic speech recognition syst...

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6th International Conference on Computing, Communication and Security, ICCCS 2021

作者： Joshi, Saurav Kumar, Vishal Machine Learning Engineer Convin.ai India Research Group Dehradun India Bipin Tripathi Kumaon Institute of Technology Dept. of Computer Science and Eng. Dwarahat India

ISBN: (纸本)9781665440394

The Automatic Speech Recognition System (ASR) produces transcripts that often are misinterpreted and confuses the reader due to lack of context and punctuations. The presence of punctuation in the text improves readability and helps in better cognitive understanding. A wide variety of work has been done on English but Hindi which is the third-largest spoken language in the world, after English and Mandarin, still remains in the shadows. This paper aims to extend the technology to a wider section and introduces an end-to-end system, interpolating an automatic speech recognition system and natural language processing to produce high-quality punctuated transcriptions for the Hindi language. An ASR is implemented using the Kaldi toolkit leveraging the hybrid deep neural networks and the punctuation restoration is done with Bidirectional RNNs with an attention network. © 2021 IEEE.

关键词： Speech recognition

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Stock Market Forecasting Using LSTM

Stock Market Forecasting Using LSTM

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Communication & Computing (WCONF), World Conference on

作者： J. Aswini Dinesh S C. Lakshmipriya Lokesh Krishnaa M Siva Subramanian R Dept of Artificial Intelligence & Machine Learning Saveetha Engineering College(Autonomous) Department of Artificial Intelligence and Data Science Saveetha Engineering College(Autonomous) Department CSE S.A. Engineering College Department CSE R.M.K College of Engineering and Technology

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

In this research paper, a novel methodology for forecasting stock market trends is presented: the utilization of Long Short-Term Memory networks, which are a part of RNN network. This model effectively incorporates the delivery of dynamic datasets, model training, and the immediate distribution of the learned TensorFlow model through the utilization of deep learning. The Long Short-Term Memory is a highly effective tool for understanding complex stock market behaviors, as it captures sequential dependencies and effortlessly adapts to changing market conditions. Transparency, collaboration, scalability, user experience, and security are given top priority in the project to guarantee accessibility for users of all levels of expertise. By outperforming conventional neural network architecture in prediction and by capturing dependencies over extended time periods, the LSTM model demonstrates its value. This research may pave the way for additional financial forecasting applications, thereby enhancing the efficacy of decision-making instruments in dynamic stock market environments.

关键词： Deep learning Training Adaptation models Decision making Computer architecture Predictive models User experience Stock markets Forecasting Long short term memory

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Plant seedlings classification using transfer learning 2

Plant seedlings classification using transfer learning

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2nd IEEE International Conference on Electronic Engineering, ICEEM 2021

作者： Hassan, Esraa Shams, Mahmoud Hikal, Noha A. Elmougy, Samir Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning and Information Retrieval Kafrelsheikh Egypt Mansoura University Faculty of Computers and Information Dept. of Information Technology Mansoura35516 Egypt Mansoura University Faculty of Computers and Information Dept. of Computer Science Mansoura35516 Egypt

ISBN: (纸本)9781665418423

Agriculture is essential for human survival and remains a major economic driver in many countries around the world. Most of the living things around the world feed on vegetation produced by agriculture. Therefore, the researchers should work on developing agriculture using the most recent artificial intelligence approaches. The diagnosis of the plant diseases based on the leaf detection are currently utilized based on machine vision systems. The selective of weeding are more helpful and struggled to identify weeds on a reliable and accurate manner compared with the traditional classification workflows that are sluggish and error-prone results from classification expertise given small number of expert taxonomists. In this paper, an overview of recent attempts to classify species using computer vision and machine learning techniques are realized. It concentrates on identifying plant species using leaf images. We used a dataset containing 4, 275 images of 12 species at various growth stages. Furthermore, we present an architecture for plant seedling classification-based machine learning. Convolutional Neural Network (CNN) and transfer learning are utilized as a classification algorithm. The experimentations results based on these classifiers indicated that the proposed model achieved 0.9754, 0.9742, 0.9766, and 0.9754 in terms of Accuracy, Sensitivity, Specificity, and F-score, respectively. © 2021 IEEE.

关键词： Convolutional neural networks

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