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

作者： Benedict Vinusha, V. Varshitha Reddy, M. Nikhitha, N. Divya, D.M. R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350335095

This research project focuses on the development of a smartwatch designed to assist individuals with eating disorders in managing their health and recovery journey. The aim is to address the barriers to treatment, such as high costs and lack of awareness, by leveraging advancements in technology. The smartwatch incorporates unique features, including the analysis of heart rate, finger movements, and emotional states. Significant changes in heart rate above a predetermined threshold trigger alerts to healthcare providers, enabling timely intervention. The use of a metaverse platform allows caregivers to engage with patients virtually, providing assistance when needed. Monthly emotional trends are analyzed using LSTM algorithms to gain insights into patients' progress. Additionally, a skin conductance sensor enables the analysis of a patient's heart condition through sweat. The smartwatch was developed using Android Studio and Java, utilizing various sensors such as PPG, accelerometer, IMUs, gyroscopes, and Heart Rate Variability analysis. The integration of metaverse technology enhances connectivity and offers immersive virtual reality experiences for patients and healthcare providers, promoting better engagement and support in the recovery process. © 2023 IEEE.

关键词： Diseases

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Predicting Pedestrian Movement in Unsignalized Crossings: A Contextual Cue-Based Approach 16

Predicting Pedestrian Movement in Unsignalized Crossings: A ...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Muduli, Kaliprasana Sahu, Vikas Ghosh, Indrajit Indian Institute of Technology Roorkee Civil Engineering Department Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Civil Engineering Department & Joint Faculty Roorkee India

ISBN: (纸本)9798350383119

To ensure safe and secure coexistence between pedestrians and autonomous vehicles (AVs), AVs must be able to anticipate pedestrian behavior and respond to it. This research gathers video data from real traffic scenes to predict pedestrian crossing intentions at unsignalized crossings. Computer vision techniques such as YOLOv4, Deep SORT, and perspective transformation are employed for road user detection, tracking, and mapping image coordinates to world coordinates to prepare trajectory datasets. Using trajectory data, several features influencing pedestrian intention like walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle upstream, etc., are extracted. The dataset for this study was obtained by analyzing 1,411 pedestrians, resulting in 223,136 samples. To predict pedestrian crossing intentions, LSTM and Bi-LSTM with an attention mechanism model were built and trained to anticipate the pedestrian crossing intention at unsignalized crossing. The proposed model successfully combined the characteristics and surrounding dynamics of pedestrians to produce accurate predictions, Bi-LSTM with an attention mechanism outperformed LSTM, achieving an AUC of 95.3%, 91.1%, 89.2%, 87.5%, and 84% on the testing dataset at unsignalized crossing on the 0.6 sec, 1.2 s, 1.8 s, 2.4 s, and 3 s time horizons. These outcomes can be used to improve Connected and Autonomous Vehicle (CAV) technologies, infrastructure-to-vehicle (I2V) connectivity, and driver assistance systems to enhance pedestrian safety while navigating through pedestrian crosswalks. © 2024 IEEE.

关键词： Attention Computer vision Pedestrian Crossing Intention Prediction Unsignalized Crossings

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Advancing Optical Character Recognition for Handwritten Text: Enhancing Efficiency and Streamlining Document Management 14

Advancing Optical Character Recognition for Handwritten Text...

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

作者： Benedict Vinusha, V. Indhuja, V. Varshitha Reddy, M. Nikhitha, N. Reddy, Naga Venkata Siva R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350335095

This research paper explores the challenge of converting handwritten text into editable digital text, in both native and English languages. We propose optical character recognition (OCR) as a solution, leveraging algorithms and machine learning models to recognize and convert text into various digital formats. The paper highlights the significance of OCR in digitizing paper documents and streamlining document management across diverse industries. Specifically, the study focuses on developing OCR software capable of identifying regional languages, providing a comprehensive assessment of its precision and effectiveness. The findings demonstrate how OCR technology can significantly improve efficiency and cost-effectiveness for businesses dealing with large volumes of paper documents. Our approach involves using image inputs, analyzing them with Keras and OpenCV, and implementing Convolutional Neural Network (CNN) techniques and Natural Language Processing (NLP) libraries. © 2023 IEEE.

关键词： Cost effectiveness

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Internet of Animal Health Things (IoAHT) Framework for Clinical Mastitis Detection in Dairy Cows 1

Internet of Animal Health Things (IoAHT) Framework for Clini...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Ankitha, K. Venugopala, P.S. Kunder, Harish Shetty, Ankitha NMAMIT Nitte Dept. of Artificial Intelligence and Data Science Karkala India Alvas Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Moodbidri India

ISBN: (纸本)9798350306637

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production. © 2023 IEEE.

关键词： Internet of things

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

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IEEE Transactions on Multimedia 2025年 27卷 3343-3355页

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Zhang, Kaihao Chen, Ting Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China Australian National University School of Computing CanberraACT Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications. © 1999-2012 IEEE.

关键词： Restoration

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Blockchain-Empowered Asynchronous Federated Reinforcement Learning for IoT-Based Traffic Trajectory Prediction

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IEEE Internet of Things Journal 2025年第11期12卷 17095-17109页

作者： Wang, Bin Tian, Zhao Tang, Fengxiao Pan, Heng She, Wei Liu, Wei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450002 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Central South University School of Computer Science and Engineering Changsha410083 China Zhongyuan University of Technology School of Cyber Space Security Zhengzhou450007 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou Key Laboratory of Blockchain and Data Intelligence Zhengzhou450002 China

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues, such as driving safety and multivehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. First, we propose a curiosity proximal policy optimization (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical gated recurrent unit (GRU) and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based dynamic group practical Byzantine fault tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction. © 2014 IEEE.

关键词： Federated learning

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Smart Technology for People with Food Disorder 7

Smart Technology for People with Food Disorder

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Benedict Vinusha, V. Reddy, Medarametla Varshitha Divya, M.D. Johnson, Sandra R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350343144

Eating disorders are severe mental illnesses that impede a person's relationship with food, eating, and body weight or shape. Eating disorders are classified into numerous categories, including anorexia nervosa, bulimia nervosa, binge-eating disorder, and others. It might end up in heart disease at a young age due to mental stress, purging, and imbalance in body functionality. According to NEDA, people with food disorders have 50% of chances to die from heart disease. Nowadays, many people, particularly teenagers, are affected by food-related issues. This paper aims to address people suffering from food allergies by proposing an interactive application using Android Studio and Java. Our solution allows people suffering from food disorders to track their health condition, and food sensitivities along with counseling and guidance from psychologists and nutritionists. Our solution has many features which take care of the all-around treatment for a particular person. Our application has features like an Emotional questionnaire, Medical history, Meal log, Rehab center information, Dietician and psychiatrist appointments, awareness blogs, and motivational speeches. Our application is on top of AWS services, the KNN algorithm for recommendations. © 2023 IEEE.

关键词： Studios

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Intelligent Assistant for Multivariant Analysis 26

Intelligent Assistant for Multivariant Analysis

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26th International Conference of the Catalan Association for artificial intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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Enhancing Healthcare: Incorporating Blockchain into Electronic Health Records 7

Enhancing Healthcare: Incorporating Blockchain into Electron...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Saratkar, Saniya Langote, Meher Chaudhari, Aarti Thute, Trupti Raut, Rohini Thakre, Gayatri Datta Meghe Institute of Higher Education And Research Faculty of Engineering And Technology Department of Artificial Intelligence And Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798350359299

Handling large amounts of medical data on a blockchain can be challenging, maintaining patient privacy while ensuring data accessibility requires careful design. The healthcare sector is constantly changing, and in order to meet the issues of data security, interoperability, and patient empowerment, it is essential to integrate innovative technology. With its reputation for being decentralized and impervious to tampering, blockchain technology has become a disruptive factor in the redesign of electronic health records (EHR). Blockchain technology has long been a fascinating field of study, and many different businesses have benefited from its applications. Similar to this, the security, privacy, secrecy, and decentralization of blockchain technology present enormous opportunities for the healthcare industry. However, issues with data management, security, and integrity plague Electronic Health Record (EHR) systems. This research study explores how blockchain technology might revolutionize electronic health record systems and perhaps address these problems. © 2024 IEEE.

关键词： Blockchain

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DDoS Attack Detection in IoT Environment Using Crystal Optimized Deep Neural Network 1st

DDoS Attack Detection in IoT Environment Using Crystal Optim...

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1st International Conference on artificial intelligence of Things, ICAIoT 2023

作者： Karpagavalli, C. Suganya, R. Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Information Technology Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9783031487736

Internet of Things (IoT) is the interconnection of many devices through the internet for different real-time applications. One of the major issues of IoT is Distributed Denial of Service attack(DDoS) and many research works have been carried out to circumvent the DDoS attack;however, they failed to attain the accurate classification of DDoS and normal traffic. In context with this, we propose a novel Deep Neural Network (DNN) based Crystal Search algorithm (CSA) (DNN-CSA). The modified Multilayer Preceptor (MLP) based DNN enhances the classification outcomes. Prior to classification the data are collected by sniffer tool and preprocessed using the min-max approach. Experimental analyses are carried out to analyze the performance of our proposed approach and the results are compared with other state-of-art works. The proposed methodology offers better detection accuracy, precision, recall, and F1-score for DDoS attack detection and also effective results in terms of throughput, energy consumption, and memory utilization. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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