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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Malviya, Sachin Dubey, Shivendra Verma, Dinesh Kumar Sharma, Anubhav Nair, Rajit Chauhan, Pushpraj Singh Computer Science and Engineering Parul University of Engineering and Technology Parul University Gujrat Vadodara India Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna India Ies Institute of Technology and Management Ies University Bhopal Department of Computer Science and Engineering Madhya Pradesh 462044 India School of Computing Science & Engineering Vit Bhopal University Madhya Pradesh India Sagar Institute of Science and Technology Department of Computer Science and Engineering Madhya Pradesh Bhopal India

ISBN: (纸本)9798350343274

Through computational methods and natural language processing, sentiment analysis recognizes and classifies the sentiments conveyed by personal statements. The current effort aims to apply entity semantic analysis to construct a test case for a healthcare system for individualized medication. To assess if a patient's response to a cure, service, therapy, etc., is positive, negative, or neutral pharmaceutical data analysis is used. After a more thorough investigation of the patient surveys and an excellent diagnostic decision system, the gathered orientations were compared. To classify the pharmaceutical data analysis and contrast, sentiment polarity methods based on machine learning are also applied. Support vector machines (SVM), random forest classification, linear SVC, and multinomial NB are the most used analytic Sklearn techniques. It has been discovered that the SVM method achieves better accuracy than other algorithms. © 2023 IEEE.

关键词： Support vector machines

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Heat Stress Forecasting and Mitigation in Outdoor Worker Safety Using Gradient Boosting and IoT Technologies 1

Heat Stress Forecasting and Mitigation in Outdoor Worker Saf...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Dhivya, K. Gurulakshmanan, Gurumoorthi Vimaladevi, S. Madhavi, Buddaraju Rekha Senthilkumar, S. Srinivasan, C. SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Tamil Nadu Chennai India Mphasis Corporation TX United States Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Commerce Avadi Tamil Nadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Workers are more vulnerable to heat stress when working outdoors. Thus, accurate predictions and measures to reduce its effects are crucial. This paper presents a new method that combines Gradient Boosting algorithms with Internet of Things (IoT) technology to combat heat stress, a serious problem for outdoor workers. It uses Gradient Boosting to create a model to predict heat stress levels using several environmental variables, including humidity, temperature, and sun radiation. An IoT sensor is used to gather real-time environmental data, improving the precision and timeliness of heat stress forecasts. By integrating several systems, it may take proactive steps to reduce the potential of heat-related diseases and injuries. These steps include changing work schedules, ensuring people drink enough, and using cooling treatments. The proposed solution uses advanced machine learning (ML) algorithms with IoT infrastructure to dramatically enhance outdoor worker safety in heat-stress-prone situations. It helps preserve outdoor workers' health and well-being by allowing prompt interventions and educated decision-making, leading to successful solutions for anticipating and reducing heat stress. © 2024 IEEE.

关键词： Diseases

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Feature Fusion-Based Deep Learning Model to Ensure Worker Safety at Construction Sites 1

Feature Fusion-Based Deep Learning Model to Ensure Worker Sa...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Gautam, Vinay Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Trivedi, Naresh Kumar Garg, Naveen Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350367720

Safety helmets must be worn at construction to avoid injuries. Manual inspection of safety helmets for every worker requires a lot of human effort which is a costlier and tedious task. Hence, visual inspection can save human effort and reduce the cost. It helps the manager to send safety reminders to the workers. Therefore, this paper describes a feature fusion method to detect safety helmets at construction sites without human intervention. Because of the complex sciences of a construction site, this paper proposed a vibrant deep-learning solution with a better accuracy rate. Here, safety helmets are identified with a feature fusion deep learning model. The features of images are extracted with a hand-crafted deep learning model and pre-trained models that are later fused into a matrix with the fusion technique. Finally, the classification was employed with a convolution neural network. The proposed model was compared with other state-of-the-art deep learning models, and it outperforms with 98.90% correct classification accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 2024 IEEE.

关键词： Convolutional neural networks

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On the Convergence of Finite Element Domain Decomposition Methods for Discontinuous Subdomain Interfaces

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4122-4127页

作者： Xin, Xi-Min Gao, Hong-Wei Wang, Shu Jian Lim, Qi Peng, Zhen Beijing Institute of Technology School of Integrated Circuits and Electronics Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China The University of New Mexico Applied Electromagnetics Group Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States NVIDIA Corporation Santa ClaraCA95050 United States University of Illinois at Urbana–Champaign Department of Electrical and Computer Engineering UrbanaIL61801 United States

In this communication, we propose a finite element domain decomposition method (FE-DDM) with Robin-type transmission conditions (TCs) at discontinuous subdomain interfaces. By definition, these discontinuous interfaces refer to impedance surfaces where electromagnetic (EM) fields exhibit discontinuities, which are common in applications of metamaterials/metasurfaces, frequency-selective surfaces, or thin dielectric layer approximation. Through a detailed convergence study, we propose an optimized set of parameters in the TC. The proposed TC provides convergence for both propagating and evanescent EM waves across subdomain interfaces and is valid for all resistivity values. Numerical experiments are supplied to validate our theoretical analysis, demonstrating the convergence and accuracy of our proposed work. © 1963-2012 IEEE.

关键词： Electromagnetic wave transmission

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ARIA: Augmented Reality and Artificial Intelligence enabled mobile application for Yield and grade prediction of tomato crops 2

ARIA: Augmented Reality and Artificial Intelligence enabled ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Balaji Prabhu, B.V. Shashank, R. Shreyas, B. Jois Narsipura, Omkar Subbaram Malnad College of Engineering Hassan Visvesvaraya Technological University Belagavi573202 India Dept. of Computer Science Viterbi School of Engineering University of Southern California Los Angeles90007 United States Dept. of Computer Science and Engineering RNS Institute of Technology Bangalore560098 India Dept. of Aerospace Engineering Indian Institute of Science Bangalore560012 India

Agriculture is the most crucial sector of the Indian economy. Lately, there has been a surge in the usage of technologies like deep learning and computer vision to make the process of agriculture modern and consequently, lessening the mistakes related to conventional processes. This work delivers a user-friendly, accessible, and novel approach for the detection, counting, and grading of tomatoes found on a farm. An Augmented Reality (AR) based mobile application is developed to obtain the images efficiently from a tomato farm in the pre-harvest stage subjected to open situations. The proposed approach uses Faster RCNN, a convolutional neural network model for detection of tomatoes from the input image on a large scale. The proposed model is trained and tested using 2083 images. The results are then analyzed for the overall performance of detection, segmentation, and classification of tomatoes. The results have examined the efficiency of the proposed mobile application and demonstrate the robustness it exhibits for the detection, grading and yield prediction of Tomatoes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Augmented reality

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Dual-Model Approach for Lung Disease Classification using Convolutional Neural Networks and Support Vector Machines 2

Dual-Model Approach for Lung Disease Classification using Co...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shaik, Mohammed Ali Fatima, Afshan Parveen, Masrath Rani, A. Soumya Mohammad, Akheel Rahim, Abdul School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Information Technology Vidya Jyothi Institute of Technology Hyderabad India Hyderabad India Department of Computer Science & Engineering Dr. Vrk women's College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Secunderabad India

ISBN: (纸本)9798331504960

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4%, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%;Precision was 94.12%;and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases. © 2024 IEEE.

关键词： Lung cancer

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Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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A Robust Security Method for Medical Image Based on 2D Schaffer Map 4

A Robust Security Method for Medical Image Based on 2D Schaf...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Sharma, Suvita Rani Kaur, Manpreet Singh, Birmohan Minocha, Sachin Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur Punjab Longowal148106 India Bennett University SCSET Greater Noida India Sant Longowal Institute of Engineering and Technology Department of Electrical and Instrumentation Engineering Sangrur Punjab Longowal148106 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350395914

In this work, a methodology has been propounded for the security of the medical images containing Confusion and Diffusion phases in which a 2D Schaffer map is utilized. The confusion phase rearranges the row and column of medical images using the sorted indexes of the chaotic sequences produced by the Schaffer map. During the diffusion phase, the chaotic sequences are utilized to alter the pixel values of the original image through the XOR operation. This phase applies to both the row and column pixel values of the images, enhancing security. The efficacy of the proposed security method has been assessed through histogram analysis, chi-square, and variance test, revealing uniformity in encrypted image pixel values. The correlation analysis shows results closer to zero and an entropy value nearly equal to the ideal 8. Moreover, the encrypted image exhibits lower Peak Signal-to-Noise Ratio (PSNR) and higher Mean Square Error (MSE) values, indicating structural dissimilarity with the original image. The Unified Averaged Changed Intensity (UACI) value exceeds 33.40% and the Number of Changing Pixel Rate (NPCR) value exceeds 99.60%, indicating resistance against differential attacks, while the Structural Similarity Measure (SSIM) values closer to zero for the encrypted image indicates structural dissimilarity. The results affirm that the propounded methodology is secure from entropy, differential, statistical, and brute force attacks. © 2024 IEEE.

关键词： Image enhancement

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