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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naidu, K. Venkatagurunatham Pillai, Anchu Krishnan Rajaprabha, C.E. Sharma R, Rajesh Ul Islam, Atowar Khasimbee, Shaik Dr M.G.R Educational and Research Institute Dept. of Computer Science and Engineering Chennai India Xavier Institute of Engineering Applied Science and Humanities Maharashtra Mumbai India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Nazareth1888 Ethiopia University of Science and Technology Meghalaya Department of Computer Science and Electronics Meghalaya Baridua793101 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798350399264

technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection. © 2023 IEEE.

关键词： Internet of things

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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A Case Study on the Generalization of Chinese Text Classification Methods based on Deep Learning 3

A Case Study on the Generalization of Chinese Text Classific...

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3rd International Conference on Robotics and Artificial Intelligence, JCRAI 2023

作者： Zhou, Yujun Fei, Qingchun Zou, Guobing Nanjing Research Institute of Electronics Technology Nanjing China School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798400707704

In the past decade, deep learning based methods have taken a dominant position in natural language processing (NLP). For almost all NLP tasks, deep learning based methods far surpassed traditional methods. Especially in the past five years, the development of deep learning methods has been particularly rapid. For example, the pre-training and fine-tuning paradigms represented by BERT have dominated the NLP field, while also driving the development of other fields such as computer vision. Nowadays pre-trained large language models (LLMs) such as GPT-3/ChatGPT further demonstrate the advantages of Transformer based deep learning methods, which can achieve good results across various problems without any specialized training. In spite of the remarkable success, their performances still underperform fine-tuned models in the task of text classification in some scenarios. Nevertheless, the LLMs are good generalist models. The goal we pursue is the deep learning methods with good generalization ability. In the case of limited computing resources and high performance requirements, the fine-tuned models remain the first choice. So how is the generalization ability of the fine-tuned models? In this paper, we explore the generalization of representative Chinese text classification methods based on deep learning. The experimental results indicate that Transformer based methods present good ability of generalization on two significant different Chinese datasets. In the current era of LLMs, this work can assist us in choosing more appropriate solutions for natural language processing tasks. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep learning

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Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

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IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

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The Necessary Requirements for Utilizing AI in Data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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Refining Object Localization from Dialogues 1

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Chinese Intelligent Automation Conference, CIAC 2023

作者： Kang, Xueze Wu, Lei Lu, Lingyun Liu, Huaping Department of Computer Science and Technology Tsinghua University Beijing China Nanjing Research Institute of Electronic Engineering Nanjing China

ISBN: (数字)9789819961870

ISBN: (纸本)9789819961863

The ability to locate objects accurately is crucial for robots’ application in large-scale environments. However, objects’ location changes constantly in practical scenes, so the robot’s knowledge of objects’ distribution needs to update correspondingly. We propose a method to refine objects’ location from dialogues between humans and robots. When a robot is searching for an object, if the information about the object’s location is insufficient, it constantly raises questions to a human informer until it acquires adequate location information. Furthermore, every possible target object found during the searching process is photographed and sent back to the human informer for judgment. If all possible locations have been searched and the target is not found, the robot requests more specific location information and starts a new search attempt after attaining the information. The interaction process repeats iteratively until the robot finally finds the target. The interactions with humans ensure the success and accuracy of searching. We deploy our method on a mobile robot and conduct language parsing experiment and locating objects experiment to evaluate the performance of our method. Moreover, we compare our method with a one-time message method from a previous work to demonstrate our advantage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Robot applications

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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An Improved Semi-Supervised Learning-Based U-Net for Underwater Image Enhancement 5

An Improved Semi-Supervised Learning-Based U-Net for Underwa...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Han, Jiqun Liu, Xinyu Tan, Dongxu Zhu, Jiehui School of Computer Science and Engineering Northeastern University Shenyang China Guangdong Institute of Intelligent Unmanned System Technology Research and Development Department Guangzhou China

ISBN: (纸本)9798350385557

The attenuation and scattering of light in underwater environments often lead to degradation issues such as image blurring, color cast, and low contrast. Due to the lack of high-quality paired datasets, the performance of deep learning-based methods is limited. To address these challenges, we propose an improved U-Net architecture based on semi-supervised learning for underwater image enhancement. First, a multi-scale input-level fusion module is proposed to allow our network to fully exploit global spatial information from input images, natural light fields, and depth maps. Second, a multi-branch hybrid convolutional attention residual block is designed to improve the network's feature extraction capability and focus on important spatial and channel information in underwater images. Finally, a semi-supervised learning approach is used to fully exploit unpaired datasets, improving the generalization ability of the network model. Through qualitative and quantitative analysis, the proposed network achieves satisfactory image enhancement results in various underwater scenarios. © 2024 IEEE.

关键词： Image enhancement

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Extreme Learning Machine for Breast Cancer Diagnosis using cloud computing

Extreme Learning Machine for Breast Cancer Diagnosis using c...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Muller, Priya Shirley Prasad, Mudarakola Lakshmi Sikrant, Poornima A Reddy, Pundru Chandra Shaker Sharma, Swati Sharma, Nipun Sathyabama Institute of Science and Technology Department of Mathematics Chennai India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Management Sciences Tamilnadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Bangalore School of Computer Science Engineering and Information Science Karnataka India

ISBN: (纸本)9798350309140

Worldwide, breast cancer ranks high among women's leading causes of mortality. The likelihood of mortality from breast cancer can be decreased with early detection and rapid treatment. Among those residing in rural regions with limited access to healthcare, machine learning on the cloud is currently playing a crucial role in illness diagnosis. Machine learning-based diagnostic systems can supplement radiologists' work as secondary readers to ensure accurate illness diagnosis, while cloud-based systems can facilitate remote diagnostics and telehealth services. The potential of artificial neural networks (ANN)-based techniques for illness diagnosis has piqued the interest of numerous academics. Among the many ANN versions, extreme learning machine (ELM) stands out as a promising candidate for tackling a wide range of classification issues. Three distinct areas of study are brought together in the suggested framework of this work: The first area where ELM is used is in the detection of breast-cancer. Next, the gain-ratio feature selection strategy is utilized to remove features that are not significant. Finally, this research offers an ELM-based CC-approach for remote breast-cancer diagnostics. Several cutting-edge technologies for illness diagnosis are contrasted with the cloud-based ELM's performance. The outcomes obtained on the Wisconsin-Diagnostic-Breast-Cancer(WBCD) dataset demonstrate that the ELM technique hosted in the cloud surpasses alternative approaches. When comparing ELM's performance in both the standalone and cloud contexts, the former proved to be superior. © 2024 IEEE.

关键词： Adversarial machine learning

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. Mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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