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2nd IEEE International Conference on Emerging Research in Computational science, ICERCs 2024

作者： Divya, s.v. venkadesh, P. Joan Pavithra, R. Amal, M.R. Lakshayaa, G. Logasundari, A. V.S.B College of Engineering Department of CSE Coimbatore India V.S.B College of Engineering Department of AI&DS Coimbatore India NPR College of Engineering and Technology Department of CSE Dindigul India Department of CSE Kottayyam India

ISBN: (纸本)9798331534950

Parkinson's Disease (PD) is a neurodegenerative disorder that affects movement, balance and coordination. It is a chronic disease that results from the degeneration of dopamine producing neurons in the brain. several artificial intelligence and machine learning techniques has been introduced for the early detection of PD. Early detection of PD is indispensable for slowing down its progress and providing the possibility to access the therapy. Therefore, the research focus on developing a hybrid framework (XGbRF) utilizing XGboost and random forests to enhance the accuracy of predicting the PD is proposed. It is hypothesized that the proposed framework will benefit from this complementary characteristic by using RF's strength in variance reduction through bagging and XGboost on bias minimization through boosting. This research was tested on publicly available datasets using cross-validation with hyper-parameter tuning for the optimization of optimal performance. In comparison, the proposed framework results in higher accuracy rate of 0.97, precision of 0.92, F1 score of 0.96 and AUC-ROC value of 0.93 than the existing classifiers. Therefore, the outcome of this research would essentially mean that the proposed framework may constitute a solid framework for medical diagnostics and serve as a better prognosis of PD. © 2024 IEEE.

关键词： Neurodegenerative diseases

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smart Farm Management Using Machine Learning 2

Smart Farm Management Using Machine Learning

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Prabakaran, s. Dharun, T. Gowsik, H. Anu Prakash, K. Kirubakaran, K. V.S.B. Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350368109

This article describes the integration of machine learning (ML) with smart agriculture management to improve agriculture through data-driven insights and automation. Machine learning algorithms analyze data from a variety of sources, including sensors and drones, providing farmers with recommendations on crop management, resource allocation and pesticides. Tracking learning helps crop classification and disease detection, while learning from tracking predicts yield and finds anomalies. Reinforcement learning makes robotic farming possible. The challenges include data quality, security and integration, but the benefits are huge;increased usage, decreased resource usage, and increased revenue. © 2024 IEEE.

关键词： smart agriculture

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Explainable AI for Fraud Detection: Enhancing Transparency and Trust in Financial Decision-Making 2nd

Explainable AI for Fraud Detection: Enhancing Transparency a...

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2nd International Conference on Emerging Wireless Technologies and sciences, ICEWTs 2024

作者： Kapale, Rahul Kishor Deshpande, Prasanna shukla, shyam b. Kediya, shailesh Pethe, Yoginee s. Metre, sujit Gajananrao Shri Ramdeobaba College of Engineering and Management Nagpur India School of Ports Terminal Management and Logistics Symbiosis Skills and Professional University Kiwale Pune India BVDU IMED Pune India School of Logistics and Supply Chain Management Symbiosis Skills and Professional University Pune India Department of Computer Science and Applications Ramdeobaba University Nagpur India S. B. City College Nagpur India

ISBN: (纸本)9783031878855

Within the past few a long time, utilizing Counterfeit Insights (AI) to discover extortion in managing an account frameworks has gotten a parcel of consideration since it seem make things more secure and more effective. but when AI models are utilized, particularly in critical areas like keeping money, there has to be a adjust between exactness and openness. This exposition looks at how Explainable AI (XAI) can offer assistance unravel these issues by making trick discovery frameworks less demanding to understand. This will lead to more openness and trust within the ways that monetary choices are made. The most reason of this think about is to see into how XAI strategies can offer assistance us get it how complex AI models utilized for trick spotting truly work. by clarifying how these models make choices, XAI not as it were increments openness but too makes a difference individuals who have a stake within the matter, like budgetary controllers, bookkeepers, and conclusion clients, get it why certain choices are made. This information is exceptionally critical for making beyond any doubt that individuals are held dependable and take after the rules set by the basel Committee on Managing an account supervision (bCbs) and the Common Information security Control (GDPR). The think about moreover looks at diverse XAI strategies and how they can be utilized to find fraud. These incorporate model-agnostic approaches such as LIME (Nearby Interpretable Model-agnostic Clarifications) and sHAP (sHapley Added substance Clarifications). These strategies grant you data around how critical highlights are by appearing you which components have the greatest affect on foreseeing extortion. These sorts of findings not as it were offer assistance to test and progress models, but they moreover allow money related educate more data to assist them make superior choices approximately how to handle hazard and halt tricks. This consider moreover talks about the benefits of XAI that go beyond

关键词： budget control

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Electromagnetic Channel Modeling and Capacity Analysis for HMIMO Communications

arXiv

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arXiv 2025年

作者： Wei, Li Yuan, shuai s.A. Huang, Chongwen Zhang, Jianhua bader, Faouzi Zhang, Zhaoyang Muhaidat, sami Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates KU 6G Research Center Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada KU 6G Research Center Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna arrays

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Early Classification of Lung Cancer based on Cell Morphology Features 1st

Early Classification of Lung Cancer Based on Cell Morphology...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： byeon, Haewon soni, Mukesh Deb, Nabamita Rivera, Richard sharma, Nilesh vijay Kaushal, Chetna Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Department of Information Technology Gauhati University Guwahati India Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Artificial Intelligence and Data Science SNJB′s Late Sau. K. B. Jain College of Engineering Nashik Chandwad India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

In response to the challenges of limited annotated lung cancer pathology images with complex cell morphology, we propose a lung cancer pathology image classification method based on contrastive learning of cell morphology features. This approach integrates highly reliable unlabelled data into the training data through contrastive learning to address the issue of insufficient labelled data. building upon the foundation of k-nearest neighbour contrastive learning, we introduce farthest and nearest neighbour contrastive learning, simultaneously using the farthest and nearest neighbour images for contrastive learning. This enhances the performance of contrastive learning by increasing the difficulty of positive sample learning and diversifying the dataset. We employ a ResNet50 encoder with deformable convolutions and dynamic convolutions to improve the extraction of cell morphology features. Experimental results demonstrate that, especially when labelled data is scarce, our classification method effectively leverages cell feature information from both labelled and unlabelled cancer pathology images, resulting in superior classification performance compared to existing methods. When applied to 80%, 60%, and 40% labeled data, respectively, CCCM outperforms other approaches with superior performance metrics, reaching F1 score of 0.9584, 0.9488, and 0.9386. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Lung cancer

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Tramline surveil system Escorting Liability Observation and Localization 2

Tramline Surveil System Escorting Liability Observation and ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： bhavadharani, K. Dheepa, M. Dharshini, M. bangadharshini, D. Dhivyabharathi, N. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

Our robotic system, which combines IoT monitoring and Arduino- based technology to detect obstacles and cracks, is important for railway track inspection. The robot's sensors are designed to match the characteristics of train tracks, ensuring accurate track defect detection and reliable obstacle recognition. This innovative approach provides an automated, low-cost solution for spotting potential hazards, with the purpose of increasing railway track maintenance efficiency. by seamlessly integrating the robot's real-time data collecting into the IoT platform, railway officials will be able to remotely monitor track conditions and perform maintenance measures as needed. Our effort addresses critical safety and maintenance challenges to create a more dependable and safer rail network by combining cutting-edge technology with railway infrastructure requirements. by offering an autonomous, low-cost, and highly sophisticated design that enhances the security and effectiveness of railroad foundation perpetuation, this innovative electronics seeks to transform railroad path inspection procedures. We hope to modernise train networks and provide a reliable, secure way of transport with the aid of this initiative. © 2024 IEEE.

关键词： Liquid crystal displays

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Eye Detection Wheelchair with smart Monitoring Using Deeplearning in CNN 2

Eye Detection Wheelchair with Smart Monitoring Using Deeplea...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Muthuchelvan, R. Divagarvasu, P. Gopinath, s. Gokul Prasanth, v. Gowtham, J. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

Individuals with severe cervical spinal cord injuries often face important challenges in interacting with their environment, including difficulties using smart phones or operating powered wheel chairs. For those with tetraplegia, Eye-tracking technologies have emerged as a crucial tool for enhancing control and convey. However, confinement in accuracy, practicality, an calibration time often plague conventional eye-tracking devices. Researchers have explored Convolutional Neural Networks(CNNs) as a potential solution to address these shortcoming in AI-powered eye tracking system. CNNs a type of deep learning architecture excel at recognizing complex pattern in a data, leading to more precise and reliable eye tracking. An mostly good way to argument the eye tracking correctness method. This technique outline a more reliable and result way to interact with digital mechanism by using deep learning algorithms to admit and track eye movements based on blink count. This technology has the power to conversion the way we interact with digital technologies, greatly improving their usefulness and accessibility for those who suffer from confusion or impairments. For human beings who have trouble with standard eye tracking because of motor restrictions, vision depletion, or other reductions © 2024 IEEE.

关键词： Convolutional neural networks

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Data security in Communication using blockchain and Key based Protocols 2

Data Security in Communication using Blockchain and Key Base...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Prabakaran, s. Harinivaash, K. Prasad, R. Kaviyan, J. Rabin, D. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

The development of global communication networks depends on satellite communication. Due to its lower stability and reach, satellite communication networks have garnered a lot of attention recently as a means of reducing the limitations of terrestrial networks. Numerous inconveniences exist, including inadequate storage space, security, and data processing capacity. Illegal entry turned out to be the difficult one. satellite physical limitations in terms of available power and area result in limited data processing capability, storage and security limitations, and the data may be exposed to alteration or contamination by intruders. since satellite communication has become more crucial in the development of global communication networks, there have been worries regarding its security. This project proposes to use the QKD protocol, which relies on identification and privacy protection, along with blockchain technology to create A network of satellite communication. To accomplish this, an architecture that consists of conventional and constrained devices connected to the blockchain via a wireless heterogeneous network has been developed. The communication is conducted by registration, authentication, and revocation. After receiving the data from the satellite, the terrestrial base station will log all important parameters on the distributed blockchain and remove any certificates belonging to rogue nodes. The proposed Chain and QKD system, which is based on satellites, offers a high degree of security for rapidly growing applications such as self-driving cars, future 6G and larger networks, and the Internet of Things. © 2024 IEEE.

关键词： satellite communication systems

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Advanced Data Mining to Explore the Effects of brain Due to schizophrenia 2

Advanced Data Mining to Explore the Effects of Brain Due to ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： sangeetha, M. Deepika, s. Rajeshwari, G. Priyadharshini, b. souparnika, C.s. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

The paper investigates changes in microstate dynamics across various schizophrenia stages. Changes in microstate dynamics across schizophrenia stages can be detected through K-means clustering of EEG data. Machine learning models for classifying different stages of schizophrenia include random forest, gradient boosting, and XGboost. Results show that there are significant changes in microstate dynamics during schizophrenia phases and this shows changes in brain processing. Furthermore, it can be noted from the use of machine learning models that their accuracy levels are quite impressive when it came to distinguishing different phases of schizophrenia through patterns generated by activities taking place inside our brains which are obtained from electroencephalography analysis. Evidently, such a statement reflects how a combination between machine learning capabilities with electroencephalography research has been used so far for this purpose which may imply a better understanding regarding its neurological underpinnings or else creation of early diagnostic devices before treatment commences. © 2024 IEEE.

关键词： Electroencephalography

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The Identification of Health of Heart Through Predictive ML based Technique in Integration with Existing system 2

The Identification of Health of Heart Through Predictive ML ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Prabakaran, s. Harish, s. Tamilselvan, b. Praveen Kumar, b. Gobinath, b. V.S.B. Engineering College Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350368109

New classes of diseases include;cardiovascular diseases: These are diseases associated with the heart and blood vessels and they are some of the classes of emerging infectious diseases that threaten human health around the world. for public health, is a big blow to complicated the issue that this is becoming a problem gradually, many researchers have been developing complicated systems that integrate patient's electronic health records, as well as many others, and artificial neural networks algorithms in a bid to produce high diagnostic results. most of the frequent artificial intelligence particularly in a context where full patient data are instantiated for the purpose of inferring the heart diseases. and thereby merely claiming the most primordial facets of our population's health. describes with exemplary accuracy the use of four separate classification methods: The used models in the current study are a) Multilayer Perceptron (MLP) b) RandomForest (RF) c) support vector Machine (svM), and d) XGboost. before the development of the conceptual models, this study went through pre-processing and doing features extraction to these analyses These were done in line with what is regarded as the best approach when doing the last phase in a given research process. When evaluating the results, the following standards were used: accuracy, precision, recall, F1-score as it has been mentioned earlier, the XGboost model shows a very high level of effectiveness The accuracy rate is rather high, 88%, which means the effectiveness of the instrument is high. Thus, this research study will fill this research gap of knowledge, as mentioned earlier, in a way that is appreciable. is a clear step in the right direction in addressing the high error rate that is associated with cardiovascular diagnosis while at the same time enhancing the validity with the MLP as well as the svM. © 2024 IEEE.

关键词： support vector machines

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