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Movable Superdirective Pairs: A Phase Shifter-Free Approach to mmWave Communications

IEEE Wireless Communications Letters 2025年第6期14卷 1806-1810页

作者： Han, Liangcheng Yin, Haifan Gao, Mengying Zhang, Rui Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore

In this letter, we propose a novel Movable Superdirective Pairs (MSP) approach that combines movable antennas with superdirective pair arrays to enhance millimeter-wave (mmWave) communications. By controlling the rotation angles and positions of superdirective antenna pairs via low-power actuators on the user side, the proposed MSP approach maximizes the received signal-to-noise ratio (SNR) of multipath signals without the need for phase shifters, thereby reducing hardware costs and insertion losses. An efficient algorithm based on alternating optimization and gradient projection is developed to solve the non-convex rotation–position design problem. Moreover, MSP is shown to be robust in both line-of-sight (LOS) and non-line-ofsight (NLOS) channels. Simulation results demonstrate that MSP achieves significant performance gains over fixed-position arrays, while maintaining low system complexity. © 2012 IEEE.

关键词： Microwave antennas

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A Review on Applications of IoT in Healthcare 3

A Review on Applications of IoT in Healthcare

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Thakare, Vedant Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

This article examines how the Internet of Things (IoT) is rapidly transforming the medical field. IoT technologies are revolutionizing patient care, increasing productivity, and boosting results in a variety of ways, from wearable technology and telemedicine to smart hospitals and personalized medicine. Predictive analytics, remote patient monitoring, and the use of AI and machine learning for more precise diagnosis and treatment regimens are important applications. The future of IoT in healthcare is extremely bright, with developments in 5G, AI, and other cutting-edge technologies set to further transform the sector and enhance human health, even while issues like data security and privacy still need to be resolved. © 2025 IEEE.

关键词： Electronic health record

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The Role of AI and Machine Learning in Cancer Diagnosis and Treatment 4

The Role of AI and Machine Learning in Cancer Diagnosis and ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gawande, Tejas Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artifical Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. The usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. The majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. The success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. The analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. The primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. The combination of better data governance toget

关键词： Federated learning

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E2FP-Net: Elevated Fusion Feature Pyramid Network for Alzheimer Detection

E2FP-Net: Elevated Fusion Feature Pyramid Network for Alzhei...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Devi, M.Kiruthiga Ramadass, Parthasarathy Karthikeyan, B. Mahendran, Rakesh Kumar Kavitha, E. Sanmugasundaram, R. Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Panimalar Engineering college Information Technology India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Dr.M.G.R Educational and Research Institute Department of Physics Chennai India Rajalakshmi Institute of Technology Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540685

Alzheimer's Disease (AD), a neurogenerative disorder which is a progressive disease and it affects the cognitive function of the brain and aids in behavioral changes and loss of memory. AD a challenging and a complicated condition in which enhanced diagnosing method, treating method is investigated by the researchers. Treating the symptoms of AD can improve the quality of life to the individuals. Diagnosing methods of AD may include Neuro imaging, CSF analysis, Cognitive testing, Genetic testing etc.., By integrating the MRI images and genetic images we propose an Elevated feature pyramid network based on Adaptive hypergraph neural network. Furthermore, the GNN involves in the process of feature extraction of MRI images and Genomic data. Our proposed framework has the following process for detecting AD: A) Twin data pre-processing B) E2FP based Feature extraction C) Node mingle fusion. The feature extraction process has 3 subprocess includes a) Region based feature extraction for MRI images using Adaptive HGNN b) Mid-Level Feature Extraction for Genomic data using Adaptive HGNN c) Amalgamation of Global Feature. Our model performs well in terms of accuracy, sensitivity and specificity. This model has the achieved a great interpretability in genomic data and by enhancing the contrast the MRI data shows a best accuracy. © 2024 IEEE.

关键词： Deep learning

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Personalizing nutrition and recipe recommendation using attention mechanism with an ensemble model

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Clinical eHealth 2025年 8卷 66-77页

作者： Chaudhari, Shilpa Rane, Archana Kumar, Amala Rashmi Department of Computer Science and Engineering M S Ramaiah Institute of Technology India (Affiliated to VTU) Karnataka Bangalore India Department of MCA K. K. Wagh Institute of Engineering Education and Research Maharashtra Nashik India

Nutrient management in the context of this proposed work aims to quantize the consumption of essential nutrients in an efficient format such that it leads to a healthy and balanced lifestyle. This paper presents an intelligent nutrition management and recipe recommendation system tailored to individuals’ nutritional profiles, using an ensemble model augmented by an attention mechanism. The system quantifies user nutritional deficiencies based on blood analysis and personal preferences, generating personalized food and recipe suggestions to address these gaps. By integrating multiple supervised learning algorithms such as Random Forest, XGBoost, and MLP, the model dynamically prioritizes nutrients relevant to the user's needs. Leveraging data from the National institute of Nutrition, recipes are recommended in video format, aiming to enhance users’ health and dietary habits. The proposed model outperforms baseline systems in detecting nutritional deficiencies and offers efficient, personalized recipe recommendations through a user-friendly web and mobile interface. © 2025 The Authors

关键词： Attention Mechanism Ensemble model Euclidean norm Nutrition management Recipe recommendation Supervised Learning

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models 3

Hyperspectral Images for Crop Prediction using Machine and D...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Mahalakshmi, R. Kumutha, K. Sakthivanitha, M. Lakshmi, R. Bagavathi Sirajudeen, Mohamed Padmanabhan, Sankar Vels Institute of Science Technology & Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology Advanced Studies Department of Computer Applications-PG Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Computer Application Tamil Nadu Chennai India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai India Nilgiri College of Arts and Science Pg and Research Department of Computer Science Tami Nadu The Nilgiris India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data. © 2025 IEEE.

关键词： Classification Deep Learning(DL) Hyperspectral Images(HSI) Machine Learning(ML)

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Optimal Design and Mechanical Simulation of Rubber bushing with Convex Hull Structure Based on Bionics

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Journal of Bionic engineering 2023年第5期20卷 2018-2029页

作者： Ce Liang Min Li Jicai Liang Shaoqiang Wang Qigang Han Yi Li Key Laboratory of Automobile Materials Ministry of Educationand College of Materials Science and EngineeringJilin UniversityChangchun130025China Roll Forging Research Institute Jilin UniversityChangchun130025China School of Computer Science and Technology Changchun UniversityChangchun130022China

Inspired by the safe landing of cats falling from high altitudes,a bionic flexible rubber bushing structure is proposed and its motion characteristics are systematically studied to explore its potential application in the suppression of *** convex hull structure on the bushing surface is abstracted from the cat’s claw pad,and the hyper-viscoelastic model is selected as the constitutive model of the rubber *** addition,the design with the best vibration damping effect is finally obtained by reasonably adjusting the amount of radial compression and distribution of bionic ***,under the same conditions,the test results of the dynamic characteristics of the bushing verify the accuracy of the simulation *** results show that the convex hull bionic structure designed in this paper can effectively change the motion characteristics of the rubber bushing under various working conditions,which provides new inspiration or potential possibility for the design of rubber bushing in the future.

关键词： Rubber bushing Bionic design Finite element analysis Vibration isolator Radial compression

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Observing Different Machine Learning Approaches for Students’ Performance Using Demographic Features 6th

Observing Different Machine Learning Approaches for Students...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Srivastava, Neeraj Kumar Pandey, Prafull Mishra, Manoj Kumar Mishra, Vikas Department of Computer Science and Engineering United Institute of Technology Prayagraj Naini India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj Naini India Department of Computer Applications United Institute of Management Prayagraj Naini India

ISBN: (纸本)9789819930098

The use and importance of educational data mining (EDM) are growing very fast as the entire world is going digital. The predictions of EDM [1, 2] are playing very important role not only in improving the academic performance of individual students but also are very useful for institutions, policy makers, administrators to plan and design new policies that will certainly help them to achieve high academic performance of their students. On this basis, this paper approaches students’ performance in secondary school of two Portuguese schools. The informational elements comprise demographic, societal, and educational [3] features. Although there are many ML [4, 5] algorithms, yet in this paper, five ML classifiers such as random forest, Naïve Bayes, logistic regression, decision tree, and support vector machine have been used to predict the students’ performance that on the basis of given features, whether a student can be passed in secondary education or not. On the dataset, the Naïve Bayes classifier gives the best performance than other classifiers, when the dataset is partitioned into 80–20 ratio as training and test datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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A Review on Zero Knowledge Proof Vulnerabilities in Zcash

A Review on Zero Knowledge Proof Vulnerabilities in Zcash

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Tyagi, Shobha Manav Rachna International Institute of Research and Studies School of Engineering and Technology Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350344387

The Zcash cryptocurrency is the first well-known application of zk-SNARKs in the blockchain. The role of Zk-SNARK is to provide privacy and confidentiality. As blockchain is incapable of providing privacy in the blockchain system, the researchers are banking at Zero Knowledge Proof protocols as the solution to the privacy problem. The Zero Knowledge Proof is a relatively new technique. Hence, currently, it comes with many risks and vulnerabilities. This paper mentions risks in cryptocurrency with Zero Knowledge Proof, primarily of Zcash. The discussed vulnerabilities are information leakage in transactions, trust risk, implementation vulnerability, and other security issues. © 2023 IEEE.

关键词： Blockchain Privacy Security Zero-Knowledge-Proof zk-SNARK

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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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