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ENERGY EFFICIENT SECURITY PROTOCOL FOR HEALTHCARE INTERNET OF THINGS

Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第12期83卷 65-78页

作者： Babu, Ravi Mahesh Malluvalasa, S.N. Lakshmi Devi, M. Kalpana Karthik, Sagirala Satya Rakesh, M. Prasad, Satamraju Krishna 522508 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering Department of Computer Science and Engineering Newton’s Institute of Engineering Guntur Andhra Pradesh Macherla India

The Energy Efficient Security for Internet of Things (EESP-IoT) protocol aims to achieve a harmonious equilibrium between energy preservation, security, and network optimization within the ever-changing domain of the internet of things (IoT). EESP-IoT coordinates the actions of IoT devices where each device independently optimizes its energy usage throughout the network to ensure stability and equity. Adaptive techniques such as power optimization, alternating between sleep and wake modes, and energy harvesting reduce energy inefficiency, while assuring security by using lightweight specifically designed cryptographic techniques. By including energy and security considerations, the protocol achieves both efficiency and resilience against a range of security threats. EESP-IoT exhibited exceptional energy conservation skills, while maintaining comparable levels of throughput and latency. The protocol underwent testing in a simulated environment to assess its ability to handle a large number of devices and its effectiveness in various network circumstances. The findings confirmed that EESP-IoT is capable of maintaining a high degree of network efficiency and strong security procedures, while considerably prolonging the lifespan of IoT devices. © 2024 by Begell House, Inc.

关键词： Energy utilization

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3D E-commerce Using AI Depth Algorithm Augmented Reality 8th

3D E-commerce Using AI Depth Algorithm Augmented Reality

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Silviya, S. H. Annie Devi, S. Renuka Sriman, B. Aswinrani, M. Raja, S. Sunitha, T. Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9789819784219

In today's dynamic world of online shopping, augmented reality (AR) integration has emerged as a game-changing innovation. It transcends the limitations of traditional online shopping by harnessing AR technologies, such as Google's ARCore, to create immersive 3D product models. These models empower customers to virtually interact with and visualize products in their own environment, effectively bridging the experiential gap with in-store shopping. Employing AR software development kits and game engines, these 3D models seamlessly come to life on customers’ devices, offering a more engaging and informative shopping experience. This not only bolsters customer satisfaction but also helps online retailers manage operational costs more efficiently. With AR’s ability to let customers virtually try on clothes, visualize furniture placements, and inspect products from the comfort of their homes, it's poised to reshape the e-commerce landscape. The ongoing technological revolution is characterized by increasingly immersive, personalized online shopping, setting new benchmarks for customer expectations and retail success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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Trajectory-Aware Task Coalition Assignment in Spatial Crowdsourcing (Extended Abstract) 40

Trajectory-Aware Task Coalition Assignment in Spatial Crowds...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Xie, Yuan Wu, Fan Zhou, Xu Luo, Wensheng Yin, Yifang Zimmermann, Roger Li, Keqin Li, Kenli College of Computer Science and Electronic Engineering Hunan University Hunan China Institute of Data Science National University of Singapore Singapore State University of New York Department of Computer Science New Paltz United States School of Data Science Chinese University of Hong Kong Hong Kong A STAR Singapore

ISBN: (纸本)9798350317152

With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. To tackle the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms. © 2024 IEEE.

关键词： Trajectories

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Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

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computers, Materials & Continua 2022年第3期70卷 5107-5132页

作者： Jung-Hwa Kim Jin-Woo Jeong Department of Computer Engineering Kumoh National Institute of TechnologyGumi39177Korea Department of Data Science Seoul National University of Science and TechnologySeoul01811Kore

Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction ***,previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye *** makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics:1)users’continuous movements,2)various lighting conditions,and 3)a limited amount of available *** address these issues,we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze *** proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned *** this end,we implemented a deep learning pipeline that can handle different types and combinations of *** proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze *** various experiments,we found that 1)an infrared-modality provides more useful features than a depth-modality,2)multi-view multi-modal approaches provide better accuracy than singleview single-modal approaches,and 3)the proposed estimators achieve a high inference efficiency that can be used in real-time applications.

关键词： Human-computer interaction deep learning head-gaze estimation indoor monitoring

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Advancements in AI-Assisted Plant Disease Diagnosis 4

Advancements in AI-Assisted Plant Disease Diagnosis

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chhabra, Anmol Saroch, Tarun Gupta, Ruchika Shukla, Aditya Raghav Chandigarh University Centre of Excellence-Data Science and Cognitive Computing Computer Science & Engineering Department Mohali India

ISBN: (纸本)9798350306118

Plant disease detection is a crucial task in agriculture to ensure healthy crop production. It is vital to identify plant diseases early in order to avert economic and environmental damages. A Machine learning-based approach including Deep Learning techniques for identifying plant disease was developed using Grape leaf images in this work. Inception v3, Squeezenet, VGG-16, and VGG-19 are some embedders that are used in the suggested method to convert raw image data into a fixed-length vector representation, also known as a 'embedding' or 'feature vector.' Our research concludes that CNN stands out with exceptional accuracy above 99.70% compared to classifiers like Naive Bayes, Logistic Regression, and SVM. Additionally, Inception V3 proved to be an efficient feature extractor, enhancing feature representation and reducing processing time. © 2023 IEEE.

关键词： Embeddings

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Driver Snoozing System with Infrared Technology 3

Driver Snoozing System with Infrared Technology

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

作者： Rani, Rupa Bisht, Deepanshu Mittal, Divyansh Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350342338

This project aims to mature a Driver Snoozing System (DSS) that provide alertness to drivers when they are fatigue, both in day time and night time. A regular camera is used to monitor the driver's face and eyes to check their alertness level. The system sounds an alarm when it detects signs of tiredness. This system's main goals are to improve traffic flow, avoid accidents caused by tired drivers, warn drivers in advance, and reduce the financial burden of traffic accidents. Additionally, it will lessen the strain on healthcare systems, aid in preventing traffic-related injuries and fatalities, and enhance the quality of life for people and communities. The purpose of this study was to create a technological solution for the problem of drowsy driving, which is a major risk for road safety and a common cause of accidents. In this article, the proposed Driver Snoozing System comprises of Infrared sensor and camera which can potentially reduce the accidents caused by driver fatigue because it can identify sleepy driving both in the daytime and at nighttime. Despite some limitations, it can potentially improve traffic safety and reduce the financial expense of accidents. More research and development are needed to make it more accurate and effective. © 2023 IEEE.

关键词： Alertness vs. Drowsiness Detection System Driver Snoozing System Infrared Sensor Integration Infrared Technology Machine Learning

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Multimodal Machine Learning for Early Detection of Autism in Children: Integrating Gestures and Mood Analysis 2

Multimodal Machine Learning for Early Detection of Autism in...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Puri, Chetan Ghodeswar, Ujwala Reddy, K.T.V. Sharma, Mansi Warudkar, Sahil Department of Computer Science and Design Wardha India Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Nagpur India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331528713

Autism spectrum disorder (ASD) is characterized by neurological disorders and challenges with interpersonal communication, communication, and schedule behaviour. Early distinguishing proof of ASD is vital to optimize the intervention and progress long-term results. This paper analyses the potential of numerous machine learning procedures for early location of ASD in children. We propose a novel approach that combines gesture assessment with cognitive evaluation to capture a comprehensive picture of a child's behaviour. Side effect analysis will focus on highlights such as body language, hand movements, and eye contact, which can give profitable bits of knowledge into the social and communication designs of children with ASD. Therapy will utilize facial expressions, tone of voice, and body language to survey a child's emotional state. The combination of these strategies empowers the machine learning show to recognize complex structures that will be characteristic of ASD. This consider has the potential to move forward the exactness and productivity of ASD diagnosis in children. The proposed strategy can be connected in different situations, such as during routine testing or through a portable application. Early and precise conclusion can clear the way for convenient intervention and back for children with ASD, eventually driving to positive long-term results. © 2024 IEEE.

关键词： Diseases

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JUMBF Type Checker: A Versatile Tool for Identifying JPEG Systems Parts in JPEG Images

JUMBF Type Checker: A Versatile Tool for Identifying JPEG Sy...

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2024 International Conference on Engineering and Computing, ICECT 2024

作者： Jamil, Sonain Ullah, Faiz Kwon, Oh-Jin Yaseen Lee, Jinhee Afnan Abbas, Muhammad Sohail Department of Computer Science Gjovik2815 Norway Sejong University Department of Electronics Engineering Seoul05006 Korea Republic of Department of Data Science and Artificial Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

JPEG pictures may now include complicated meta-data using the JPEG Universal Metadata Box Format (JUMBF), which requires tools to recognize different JUMBF kinds inside the image. We offer the JUMBF Type Checker, a software tool designed to meet this demand. The system distinguishes between JPEG pictures with various JUMBF information types, including JPEG Privacy and Security, JPEG 360, and JPEG Snack. The program works with MATLAB R2023a and later, provides an effective way for users to test and evaluate the type of JUMBF included in their JPEG files. Future improvements will go beyond MATLAB to include web browsers and improve dataset validation capabilities. This application is essential for researchers and practitioners in digital image processing because it provides a simple way to organize and analyze JPEG pictures with integrated JUMBF. (https://***/sonainjameel/JUMBF-Type-Checker) © 2024 IEEE.

关键词： Metadata

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