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arXiv 2024年

作者： Zhu, Xuekang Ma, Xiaochen Su, Lei Jiang, Zhuohang Du, Bo Wang, Xiwen Lei, Zeyu Feng, Wentao Pun, Chi-Man Zhou, Jizhe College of Computer Science Sichuan University China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education of China China Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates The Hong Kong Polytechnic University Hong Kong Computer and Information Science Faculty of Science and Technology University of Macau China

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness. Copyright © 2024, The Authors. All rights reserved.

关键词： High temperature superconductors

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Predictive Modeling of Dental Health Outcomes Based on Fluoride Concentrations using AI 3

Predictive Modeling of Dental Health Outcomes Based on Fluor...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swathi Pai, M. Arudra, Annepu Dankan Gowda, V. Vadar, Parashuram Shankar Jadhav, Mahadeo Ramchandra Kaur, Mandeep Nmam Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Nitte574110 India Rajiv Gandhi Institute of Technology Department of Computer Science & Engineering R T Nagar Cholanagar Karnataka Bangalore India Bms Institute of Technology and Management Department of Electronics & Communication Engineering Karnataka Bangalore India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Bharati Vidyapeeth College of Engineering Department of Applied Science Navi Mumbai India Punjabi University Department of Electronics and Communication Engineering Punjab Patiala India

ISBN: (纸本)9798350319125

Oral health is a crucial aspect of general health, and the presence of fluoride in drinking water has been consistently linked to its improvement. This work utilizes deep learning and machine learning approaches to develop prediction models that can estimate oral health consequences based on fluoride concentrations. Our analysis is based on a comprehensive dataset that includes fluoride levels and oral health indicators from several geographic locations. The dataset covers a broad range of demographic and environmental factors. The study involves a thorough data pretreatment procedure, which includes activities such as data cleaning, standardization, and feature engineering. All of these processes contribute to improving and making the input variables more relevant. The approach used encompasses a range of machine learning algorithms, including as neural networks, decision trees, and ensemble approaches, which are used to create prediction models. Thoroughly adjusting hyperparameters and using cross-validation methods are used to maximize the effectiveness of the model. © 2023 IEEE.

关键词： artificial intelligence Data Preprocessing Feature engineering Healthcare Analytics machine learning Oral Health Predictive Modeling

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AI-Optimized Placement and Routing for PCB Design

AI-Optimized Placement and Routing for PCB Design

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International Conference on Science technology engineering and Management (ICONSTEM)

作者： P. Sathyaraj S. Arulkumar A. Ajina Kedir Beshir L. Umasankar A.N. Arularasan Department of Electronics and Communication Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electrical and Computer Engineering Wachemo University Ethiopia Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India Department of Electrical and Electronics Engineering S.A. Engineering College Tiruvallur Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics.

关键词： machine learning algorithms System performance machine learning Routing Rendering (computer graphics) Real-time systems Reliability

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SecureVFL: Privacy-preserving multi-party vertical federated learning based on blockchain and RSS

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Digital Communications and Networks 2025年第03期 837-849页

作者： Mochan Fan Zhipeng Zhang Zonghang Li Gang Sun Hongfang Yu Jiawen Kang Mohsen Guizani School of Information and Communication Engineering University of Electronic Science and Technology of China School of Automation Guangdong University of Technology Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence

Vertical Federated learning（VFL）, which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions, encounters numerous privacy and security threats. Existing solutions often suffer from centralized architectures, and exorbitant costs. To mitigate these issues, in this paper, we propose Secure VFL, a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy. Secure VFL uses a permissioned blockchain and introduces a novel consensus algorithm, Proof of Feature Sharing（Po FS）, to facilitate decentralized, trustworthy, and high-throughput federated training. Secure VFL introduces a verifiable and lightweight three-party Replicated Secret Sharing（RSS） protocol for feature intersection summation among overlapping users. Furthermore, we propose a（2～4）-sharing protocol to achieve federated training in a four-party VFL setting. This protocol involves only addition operations and exhibits robustness. Secure VFL not only enables anonymous interactions among participants but also safeguards their real identities, and provides mechanisms to unmask these identities when malicious activities are performed. We illustrate the proposed mechanism through a case study on VFL across four banks. Finally, our theoretical analysis proves the security of Secure VFL. Experiments demonstrated that Secure VFL outperformed existing multi-party VFL privacy-preserving schemes, such as MP-Fed XGB, in terms of both overhead and model performance.

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Analysis of abnormalities in cardiac arrhythmia based on 12 - LEAD electrocardiography

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Measurement: Sensors 2024年 35卷

作者： Jeevitha, S. Joel, J. Sathish Kumar, N. Immanuvel Arokia James, K. Department of Information Technology Jerusalem College of Engineering India Department of Computer Science and Engineering R.M.K. Engineering College India Department of Artificial Intelligence and Machine Learning R.M.D. Engineering College India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College India

Myocardial Infarction otherwise called heart attack occurs in human beings when blood flow decreases or stops to a part of the heart which in turn damages the heart muscle. Prediction of abnormalities in cardio arrhythmia disease is done by using standard 12-lead Electrocardiography (ECG) signals, which also detects Posterior Myocardial Infarction (PMI). The QRS complex is the merged output of different parts of graphical deflection seen on a typical Electro Cardio Gram (Electrocardiography). The main purpose of the paper is to monitor and analyze particularly the Rpeak upward deflections from the QRS complex. Denoising the ECG signal is done by butter worth filter. The denoised signals are used to detect R peaks and image plotting is done by segmentation. R peak images are used to classify the abnormalities in Myocardial Infarction (MI) with the help of the CNN image processing technique. The publicly available PTB diagnostic dataset is used to classify the abnormalities in PMI. The detection of the R peaks is used to guide Cardiologists must advance the Percutaneous Coronary Intervention treatment. Prediction has been done using probability weighted average method. Troponin level has been calculated to evaluate a person's health condition which also supports in close prediction of diseases and abnormalities. From experimental analysis of proposed Probability weighted average method in troponin level (PWAMT), the accuracy scores in the ensemble model were found to be 86 % respectively. The running of algorithm took 250 s–300 s to execute the program and display the prediction results. © 2024

关键词： Electrocardiography

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Development of a machine learning Model for pulmonary carcinoma Prediction using Computer Tomography (CT) Images 7

Development of a Machine Learning Model for pulmonary carcin...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sekhar, Jampani Chandra Singh, Priyanshu Kumar, T. Rajesh Mahendran, G. Rao, Katta Subba Maranan, Ramya Nri Institute of Technology Department of Computer Science And Engineering Andhra Pradesh Guntur India R.V. College of Engineering Department of Computer Science And Engineering Karnataka Bengaluru India Sri Krishna College of Technology Department of Artificial Intelligence And Machine Learning Tamil Nadu Coimbatore India R.M.K Engineering College Department of Mechanical Engineering Chennai Tamil Nadu Kavaraipettai India Telangana Narsapur India Saveetha School of Engineering Simats Department of Research And Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350371406

In this study, computer tomography (CT) scans are used to build a machine learning model for predicting lung cancer. A dataset of 10,000 CT scan images from 900 patients was used to train a group of machine learning algorithms. The objective is to identify the most reliable algorithm for lung cancer detection. The algorithms CNN, CNN with gradient descent, VGG-19, and VGG-16 have all been used in real-world applications. Each algorithm is trained using the dataset to create a prediction model. Performance metrics including accuracy, precision, specificity, sensitivity, and F-Score are taken into account when assessing how effective the algorithms are. The results demonstrate that the CNN model outperforms the competition with a 97% accuracy rate. The superior accuracy, specificity, sensitivity, and F-Score of the CNN model enable accurate lung cancer detection in CT scan images. The outcomes demonstrate that, utilising the well-established machine learning paradigm, the CNN algorithm, in particular, has substantial potential for early lung cancer diagnosis and prediction. Correctly identifying lung cancer from CT scan images allows for the implementation of focused treatment programmes and prompt interventions, ultimately leading to better patient outcomes. The study emphasises the requirement for applying machine learning methods to identify relevant characteristics and patterns in CT scan images. machine learning models can now be used in clinical settings to support the precise detection and treatment of lung cancer. The use of powerful algorithms and extensive datasets allowed for this accomplishment. In order to validate and enhance the models that have been developed, more study is needed in this field. The models will therefore be more useful and suitable for use in actual clinical settings. Utilising larger datasets, deep learning architectures, and other possible machine learning advancements can significantly increase the precision and generalizability of

关键词： Diseases

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Protecting Medical Image Transmission: Color Secret Sharing Protocol for Enhanced Security

Protecting Medical Image Transmission: Color Secret Sharing ...

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artificial intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Balajee Maram Manikonda Srinivasa Sesha Sai Malathy Vanniappan T. Aditya Sai Srinivas Pamarthi Nagaraju U D Prasan School of Computer Science and Artificial Intelligence SR University Warangal Telangana Department of information technology KKR & KSR institute of technology and sciences Guntur Department of Electronics and Communication Engineering SR University Warangal Telengana India Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar Department of CSE Shri Vishnu engineering College for women (A) Bhimavaram India Aditya Institute of Technology and Management Tekkali Andhra Pradesh

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The safety in the transmission of medical images cannot be compromised in the digital age, as the information contained is very sensitive. This paper presents a new approach to providing security for medical image transmission using the Color Secret Sharing protocol. This is ensured by the proposed approach of dividing the medical image into many shares, each of which is encoded with independent color information, to ensure that the restoration of the original image can be done only if all the shares are combined. Thus, the risk related to unauthorized access and data breaching is much reduced during the transmission process. Security analysis and experimental validation are provided that demonstrate that the compared methods tend to efficiently resist attacks. This study embeds CSS into medical imaging systems to solve the secure and trustworthy solution on the confidentiality, integrity, and availability of patient data in healthcare communications.

关键词： Protocols Image color analysis Image communication Medical services Resists Image restoration Encryption Safety Colored noise Biomedical imaging

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Application of RFID and IoT technology into specimen logistic system in the healthcare sector 3

Application of RFID and IoT technology into specimen logisti...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Thwe Chit, Mya Myet Srisiri, Wattanasak Siritantikorn, Atchasai Kongruttanachok, Narisorn Benjapolakul, Watit Chulalongkorn University Artificial Intelligence Machine Learning and Smart Grid Technology Research Unit Bangkok Thailand Chulalongkorn University Dept. of Laboratory Medicine Faculty of Medicine Bangkok Thailand

ISBN: (纸本)9781665408622

The invention and innovation of RFID technology changed the world and many sectors (such as logistics, railways, healthcare, and so on) are now deployed with RFID technology instead of using barcode systems. With the numerous advantages, Radio Frequency Identification (RFID) got many expectations in the healthcare sector. The main objective of this research work is to implement the RFID technology in Specimen collection in the healthcare sector and the IoT (Internet of Things) network supports the transaction while the specimen test box is being delivered. The system uses a Sparkfun RFID reader to read/write patient information to the Gen2 RFID tag, which is attached to the test tube collected from the patients. When the test box is delivered to another laboratory, we develop an IoT network to know the box's temperature, humidity, and GPS location instantly, with the help of an NB-IoT shield. The major advantage of the combination between IoT technology and RFID is that the management of test box overall condition becomes much easier. To summarize, this method is highly competent in identifying the location of medical devices in real-time and reduces the time-consuming of data logging than the barcode system. © 2021 IEEE.

关键词： Internet of things

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Competent Way to Publish Heterogeneous Satellite Data on Web Deploying Semantic Technologies

Competent Way to Publish Heterogeneous Satellite Data on Web...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： Anitha Velu Raghu Ramamoorthy Manasa S M Smitha J A Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru India Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

One of the main concerns of academics and publishers is the dynamic role that information technologies play in the dissemination and distribution of satellite data online. Interpretation, interoperability, and scalability of sensor-generated heterogeneous data are crucial tasks. This can be addressed with the help of Semantic Web (SW), which has a vast number of unstructured items represented by linked data. Compression techniques help to create a more compact representation of the data, which can reduce processing over large amounts of data. This work proposes an effective C3-GCD method for publishing copious Geospatial Climate Data (GCD), which consists of three stages: Collect, Convert, and Compress. Stage I: Involves with collection of real-time data from Indian Meteorological Satellite along the southeastern coastal areas of India consisting 170 files of 648 MB with 12,82,000 records, Stage II: A machine-readable format, most likely Resource Description Framework (RDF), is derived out of the acquired heterogeneous data. Large RD F volumes result in difficulty of processing, inadequate administration, and limited scalability hence, Stage 111: A unique HDT+ compression strategy is developed by revisiting the state-of-art binary encoding. The proposed approach conserves approximately 78.96% of storage space with compression ratio of 5.67: 1 on average, allowing for the publication of copious amounts of data. Due to the proposed method's reduction of syntactic verbosity and data redundancy, any user can exchange, publish and analyze the data from anywhere open on web.

关键词： Satellites Publishing Scalability Redundancy Sea measurements Syntactics Resource description framework Real-time systems Geospatial analysis Semantic technology

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A Comparative Evaluation of Knuth-Morris-Pratt Algorithm and Large Language Models with Retrieval Augmented Generation

A Comparative Evaluation of Knuth-Morris-Pratt Algorithm and...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： V Radhamani E Chandra Blessie S. Nithya Dharssini K Prasannapathi S Department of Computing (Decision and Computing Sciences) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Artificial Intelligence and Machine Learning) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Resume screening is pivotal in modern recruitment, determining which candidates advance in the hiring process. As companies receive many applications, especially for high-demand roles, efficient and accurate screening methods are essential to avoid overlooking potential talent. This paper examines two advanced methods: the Knuth-Morris-Pratt (KMP) algorithm, known for its fast and efficient pattern matching, and Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG), which provide context-aware resume analysis. Recent studies have focused on leveraging artificial intelligence and machine learning to enhance recruitment processes. LLMs show significant promise in handling unstructured text data, understanding nuanced qualifications, and improving hiring outcomes. This paper aims to build on these advancements by directly comparing the performance of KMP and LLMs with RAG in key areas such as processing time and accuracy. While KMP excels in speed, it cannot interpret deeper meanings, whereas LLMs with RAG handle unstructured data more effectively but demand higher computational resources. Ultimately, this study finds that LLMs with RAG outperform KMP in complex resume screening tasks, offering better accuracy and scalability, albeit with greater computational costs.

关键词： Accuracy machine learning algorithms Terminology Large language models Retrieval augmented generation Resumes machine learning Natural language processing Pattern matching Recruitment

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