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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： R. Senkamalavalli Chethan Chandra S Basavaraddi L. M. Merlin Livingston N. Latha Elangovan Guruva Reddy Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Tumkur Karnataka India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

Internet of Things (IoT) technology has improved ambulance safety and efficiency. In ambulance services, Smart PPE (Personal Protective Equipment) improves patient and healthcare worker safety via IoT connectivity. In crises, ambulances are vital. However, healthcare professionals risk contagious infections and physical damage. Though vital, traditional PPE lacks real-time monitoring and communication, leaving healthcare workers exposed. These issues are addressed with Smart PPE using IoT. This novel invention adds sensors and communication devices to paramedic and EMT PPE. These sensors measure air quality, temperature, and the wearer's heart and body temperature. A centralized system can remotely monitor healthcare personnel's safety and well-being using real-time data. Smart PPE has two-way communicators and emergency alert systems. Emergency coordination is improved by ambulance crews and dispatch centers communicating seamlessly. The Smart PPE can automatically inform the team and dispatch in urgent situations, increasing response times and patient outcomes. IoT enables predictive maintenance and data analytics. Trends and dangers may be identified from gathered data, allowing proactive risk mitigation. Smart PPE can measure use and remind users to maintain and replace equipment, assuring dependability when required most. By continually monitoring conditions, allowing real-time communication, and enabling data-driven decision-making, this unique technology improves ambulance services and reduces dangers to devoted personnel.

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Sustainable Smart Agriculture Advisor through Artificial Intelligence Approach

Sustainable Smart Agriculture Advisor through Artificial Int...

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Distributed Systems, computer networks and Cybersecurity (ICDSCNC), International Conference on

作者： Shanmugasundaram Hariharan Pogula Srivani Sreenivasulu Gogula N. Venkateswaran Vinay Kukreja Jose P Dept. of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of H&S CMR Technical Campus Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Jyothishmathi Institute of Technology & Science Nustulapur Karimnagar India Centre for Research Impact & Outcome Chitkara University Rajpura India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Agriculture, constituting the backbone of India's economy and employing 70% of its population, faces critical challenges such as water scarcity, soil degradation, and unpredictable crop yields. Traditional farming methods lack efficiency, contributing to environmental impact and resource inefficiency. To address these issues on smart crop cultivation with artificial intelligence enabled crop advisor sustainable agriculture generation integrates machine learning and data analytic to offer real-time insights and recommendations. This advanced agricultural approach aims to optimize resource utilization, minimize environmental impact, and adapt to changing climatic conditions. By empowering farmers with actionable insights, Smart Crop seeks to revolutionize agriculture, promoting sustainable practices for increased resource efficiency and enhanced resilience against challenges like limited precision farming, climate variability, and yield maximization. This innovative solution endeavors to bridge the gap between traditional farming methods and modern challenges, fostering a more sustainable and productive future for Indian agriculture. The methods analyzed leads to promising results as compared with well known metrics.

关键词： Degradation data analysis Machine learning Soil Agriculture Real-time systems Water resources Faces Farming Resilience

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Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training

arXiv

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arXiv 2023年

作者： Bai, Xiaoyu Ma, Benteng Li, Changyang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Poly-technical University Xi’an710072 China Sydney Polytechnic Institute NSW2000 Australia

Universal lesion detection (ULD) has great value for daily clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have shown promising results, but demanding substantial volumes of annotated data for effective training. Nevertheless, annotating medical images is costly and requires specialized knowledge. Additionally, the diverse forms and contrasts of objects in medical images make fully annotation even more challenging, resulting in incomplete annotations. Directly training ULD detectors on such datasets can yield suboptimal results. Pseudo-label-based methods examine the data in the training set and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue. Presently, top-performing methods rely on a dynamic label-mining mechanism, operating at the mini-batch level. However, the model’s performance varies at different iterations, leading to inconsistencies in the quality of the mined labels during training. This inconsistency limits their potential for performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we introduce an innovative exploratory training strategy to assess the reliability of mined lesions over time. Specifically, we introduce a teacher-student detection model as the basis, where the teacher model’s predictions are combined with incomplete annotations to train the student model. On top of that, we design a prediction bank to record all high-confidence predictions. Each sample is trained several times, allowing us to get a sequence of records for each sample. If a prediction consistently appears in the record sequence, it is likely to be a true object, otherwise it may just a noise. This serves as a crucial criterion for selecting reliable mined lesions for subsequent retraining. Our experimental results substantiate that the proposed framework surpasses state-of-the-art methods on two med

关键词： Forecasting

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Joint Task Scheduling and Container Image Caching in Edge Computing

arXiv

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arXiv 2023年

作者： Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian Institute of Artificial Intelligence and Future Networks Beijing Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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A DWT-Based Model with CNN-based Decision Fusion Strategy for Medical Image Encryption Scheme for Healthcare Network

A DWT-Based Model with CNN-based Decision Fusion Strategy fo...

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Smart Technologies, Communication and Robotics (STCR)

作者： G. Ramkumar Osama Jamal Jamil Abed Al Jawad J. Lefty Joyson P. Nancy Navdeep Singh Rowsonara Begum Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Faculty of Economic and Administrative Sciences BIT Zarqa University Zarqa Jordan College of Business Administration University of Business and Technology Jeddah Saudi Arabia Department of Computer Science and Engineering J.P. College of Engineering Tenkasi School of Electronics and Electrical Engineering Lovely Professional University India Department of Computer Science & Engineering (Data Science) MLR Institute of Technology Dundigal Hyderabad Telengana

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Telemedicine and digital health are expanding rapidly, increasing the demand for secure and precise transmission of medical images. This study introduces an innovative hybrid model that combines DWT, CNN, and encryption techniques for safe image processing. The Proposed DWR-CNN model secures image embedding, hybrid encryption, and deep learning classification techniques. Medical images are concealed within a reference image using methods such as SVD to maintain their confidentiality while remaining unaltered. The image is subsequently transformed using DWT to enhance its resilience against attacks. A blend of AES and ECC protects the image transfer. VGG16 and YOLOv8 are to extract features and identify errors. The DWT-CNN model encourages precision and robust safety in evaluations using medical imaging datasets.

关键词： Deep learning Accuracy Telemedicine Watermarking Transforms Elliptic curve cryptography Feature extraction Discrete wavelet transforms Encryption Biomedical imaging

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A Legal Prediction Model Using Support Vector Machine and K-Means Clustering Algorithm for Predicting Judgements and Making Decisions

A Legal Prediction Model Using Support Vector Machine and K-...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： A Jaya Mabel Rani Bharathwaj K S N M Jothi Swaroopan K Hari Kumar Geetha R Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of EEE R.M.K. Engineering College Chennai Department of Computer Science St Jude’s College Thoothoor KanyaKumari District Tamil Nadu India Department of EEE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Chennai Tamil Nadu India

The legal community has displayed significant interest in harnessing predictive models to anticipate case outcomes, providing judges and attorneys with valuable tools to enhance their decision-making processes. These models offer the potential to expedite choices, offer support for arguments, and strengthen defence strategies. However, accurately predicting legal rulings and case results is a complex endeavour, involving multifaceted stages such as sourcing appropriate bulk case documents, extracting and refining data, and navigating the intricate nature of legal paper complexities. In this innovative machine learning-powered legal prediction model is introduced, named "Legal Acumen." Beyond predictions, this model also offers informed decisions based on the appropriate laws, providing an added layer of insight and value. "Legal Acumen" not only generates predictions about judgments from the Supreme Court but also incorporates a feature that aids end users: upon uploading a new case description document to a designated folder, the system rapidly comprehends its contents and generates both a prediction and a legally informed decision. The understanding indicates that "Legal Acumen" stands among the pioneering legal prediction models striving to anticipate decisions from the Indian Supreme Court. For automatic prediction of Legal Acumen used different types of machine learning algorithms such as Support vector machine, Decision Tree algorithm, Etc., But this paper proposed two-step process. First step used Support vector machine for the prediction of judgement. Then the second step explained about the punishment of the criminal based on K-Means Clustering algorithm.

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Detection and Mitigation of Vampire Attacks in Wireless Sensor networks using Weighted Hopfield Neural Network with Nature Inspired Optimal Path Selection

Detection and Mitigation of Vampire Attacks in Wireless Sens...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Famila Shanmugasundaram Hariharan N. Sujaudeen Gopu Sowjanya D. Annal Priyadarshini T. Ramala Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chenni Tamil Nadu Dept of AIDS Vardhaman College of Engineering Hyderbad India Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of CSE(Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering (AIML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of science and technology Chennai Tamil Nadu

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Wireless sensor networks (WSNs) consist of several nodes whose primary objectives are to monitor and regulate surroundings. Furthermore, sensor nodes are distributed in accordance with network utilization. The energy consumption of sensor nodes is one of the most important problems in this kind of network. In fixed-sink networks, nodes in close proximity to the sink serve as intermediaries for transmitting data from other nodes to the sink. This results in a significant decrease in sensor energy usage. Consequently, the network’s lifespan decreases. Due to their vulnerabilities, sensor nodes are prone to various attacks, including the vampire attack that poses a danger to WSN. This paper presents a novel approach for the detection and mitigation of vampire attacks using a Weighted Hopfield Neural Network (WHNN) combined with bio-inspired optimal path selection. The WHNN is employed to detect abnormal energy consumption patterns indicative of vampire attacks, leveraging its associative memory capabilities. To counteract the detected attacks, we incorporate a bio-inspired optimization algorithm, such as Aquila optimizer Algorithm (AOA), to dynamically select the most energy-efficient paths for data transmission. This dual-layered strategy not only enhances the detection accuracy of vampire attacks but also ensures optimal path selection. Due to their vulnerabilities, sensor nodes are prone to various attacks, including the vampire attack that poses a danger to WSN. The superiority of the WHNN-AOA technique is proved by simulation results, when compared to current schemes, based on performance measures such as packet delivery ratio, average throughput, detection ratio, and network lifespan, while also reducing the average residual energy.

关键词： Wireless sensor networks Energy consumption Accuracy Prevention and mitigation Heuristic algorithms Simulation Scalability Hopfield neural networks Throughput Testing

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A Virtual Assistance for Visually Impaired People to Recognize Fabric Pattern and Color Using Human computer Interaction Logic

A Virtual Assistance for Visually Impaired People to Recogni...

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International Conference on Communication and Electronics Systems (ICCES)

作者： M.S Bravishma Panicker R. Mohandas Shanmugasundaram Hariharan K. Nirmala Devi M.S.K. Chaitanya Rashmi Mishra Saveetha School Of Law Saveetha Institute of Medical and Technical Science (SIMATS) Chennai-77 Tamil Nadu India Department of ECE Chennai Institute of Technology Chennai Department of Artificial Intelligence and Data Science Vardhaman College of Engineering India Department of Computer Applications Madanapalle Institute of Technology & Science Department of CIVIL S.R.K.R Engineering College chinamiram Bhimavaram Andra pradesh Department of Applied Science & Humanities G L Bajaj Institute of Technology & Management Greater Noida

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Modern advancements in technology have paved the way for innovative solutions to assist visually impaired individuals in their daily lives. This study presents a virtual assistance system designed to recognize fabric patterns and colors using a combination of deep learning and human-computer interaction techniques. The system employs high-quality image acquisition methods, bilateral filtering, and histogram equalization to preprocess images, enhancing their quality for subsequent analysis. Convolutional Neural networks (CNNs) and Recurrent Neural networks (RNNs) are integrated to form a Hybrid Fabric Pattern Recognition Network (HFPRN) that accurately identifies fabric patterns. Additionally, the system utilizes RGB color space conversion for precise color detection. Voice command capabilities via Amazon Alexa and tactile feedback mechanisms further enhance user interaction. Experimental results demonstrate the proposed system's high accuracy, with the HFPRN achieving 97% accuracy in fabric pattern recognition and 95% accuracy in color detection. This comprehensive solution significantly improves the autonomy and quality of life for visually impaired individuals by providing real-time, accurate feedback on fabric patterns and colors.

关键词： Human computer interaction Accuracy Recurrent neural networks Image color analysis Virtual assistants Tactile sensors Fabrics Real-time systems Pattern recognition Logic

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Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System

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Journal of Field Robotics 2025年

作者： Li, Zhibin Deng, Xun Chen, Tinghui Yang, Yuhang Chen, Linlin Yang, Xiwen Hu, Zhenzhen Hu, Lun Hu, Pengwei Li, Shuai Luo, Xin Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Urumqi China School of Software Engineering Chengdu University of Information Technology Chengdu China Dazhou Key Laboratory of Government Data Security Sichuan University of Arts and Science Dazhou China School of Software Engineering Xinjiang University Urumqi China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland VTT-Technical Research Centre of Finland Oulu Finland College of Computer and Information Science Southwest University Chongqing China

Robots are frequently utilized in manufacturing, aviation, and other industries, which enhance industrial production efficiency and quality. Specifically, robots perform high-precision tasks like welding, assembly and material handling, which reduce the intensity of manual labor in factories. In the logistics field, robots automatically sort and deliver goods, thereby speeding up supply chain operations. However, the prolonged operation of robots suffers from a decline in positioning accuracy, which makes them unable to satisfy task requirements. To address this challenging issue, this study designs an efficient calibration system integrating the Levenberg–Marquardt algorithm with fuzzy proportion integration differentiation controller and radial basis function neural network. The innovations of this method include: (1) integrating the fuzzy proportion integration differentiation controller into the updating rules of Levenberg–Marquardt algorithm, which further enhances the identification performance of kinematic errors;(2) adopting the radial basis function neural network to handle the robot dynamic errors, which addresses the complexity of dynamic error sources. Extensively experimental robot positioning points are gathered on an HSR JR680 robot, and then experimental validations are conducted by using the designed calibration system. The experiments indicate that the developed algorithm outperforms these existing advanced algorithms. © 2025 Wiley Periodicals LLC.

关键词： Radial basis function networks

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Deep Learning Approaches for Road Damage Detection Using YOLOv8 and Custom Convolutional Neural Network

Deep Learning Approaches for Road Damage Detection Using YOL...

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Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Muhammad Waseem Khan Khalid Mahmood Israr Hussain Muhammad Talha Qureshi Hafiz Muhammad Sanaullah Badar Wu Gao Center for Bio-inspired System-on-Chip School of Computer Science Northwestern Poly technical University Xi'an P. R. China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan R.O.C Institute of Computing MNS University of Agriculture Multan Pakistan Henan University Kaifeng China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The identification of road damage is deemed very important to the preservation of infrastructure. Since recent development in deep learning provide great potential approaches, in this paper, three deep learning models, namely EfficientNet, CNN and YOLOv8 are considered and their performance is evaluated on a dataset with 8,586 images with labelled road damages consisting of seven subclasses including longitudinal cracks and potholes. Besides, other issues like class imbalance and variabilities in the environment were studied and considered in its assessment based on the Models accuracy, precision, recall, and F1-score. With an accuracy of 64%, YOLOv8 is the most accurate model for identifying various types of damages, particularly in identifying multiple damages present in one or more types of damage, as was seen in the high accuracy rate of a split sample of 44 images of multiple damages. Our findings showcase YOLOv8 as a strong contender for real-time road damage detection with potential of enhancement with better augmentation and fine-tuning.

关键词： Deep learning Location awareness Accuracy Roads Image edge detection Transfer learning Real-time systems data models Convolutional neural networks Reliability

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