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Electronics and Renewable Systems (ICEARS), International Conference on

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

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

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.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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DLIoMT: Deep Learning Approaches for IoMT-Overview, challenges and the future

DLIoMT: Deep Learning Approaches for IoMT-Overview, challeng...

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Harshni, V. Singh, Satyendr Indhumathi, R. Kumar, Neelam Sanjeev Chowdhury, Subrata Patra, Sudhansu Shekhar Srm Institute of Science and Technology Department of Bme Ramapuram campus Chennai India Bml Munjal University Computer Science & Engg Department Gurugram India Vadapalani Campus Chennai India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Andra Pradesh Chittoor India Deemed to be University School of Compter Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331513894

An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine learning, conventional neural networks, recognition, and fuzzy logic are a few of the subsets of AI with unique capabilities and functions that can enhance modern medical science. Such innovations simplify human interference in clinical diagnostic procedures, decision making, and medical imaging. To improve human health, connected biomedical network devices and software applications are interconnected to form the Internet of Medical Things (IoMT), the next generation of bioanalytical tools. This review paper provides in-depth analysis of the IoMT encasing its definition, technologies, applications, challenges, and future prospects. © 2025 IEEE.

关键词： Electronic health record

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Dual-logic-in-memory implementation with orthogonal polarization of van der Waals ferroelectric heterostructure

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InfoMat 2024年第2期6卷 107-117页

作者： Jingjie Niu Sumin Jeon Donggyu Kim Sungpyo Baek Hyun Ho Yoo Jie Li Ji-Sang Park Yoonmyung Lee Sungjoo Lee SKKU Advanced Institute of Nanotechnology(SAINT) Sungkyunkwan UniversitySuwonRepublic of Korea Department of Nano Science and Technology Sungkyunkwan UniversitySuwonRepublic of Korea Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea IMEC HeverleeBelgium Department of Nano Engineering Sungkyunkwan UniversitySuwonRepublic of Korea

The rapid advancement of AI-enabled applications has resulted in an increasing need for energy-efficient computing ***-in-memory is a promising approach for processing the data stored in memory,wherein fast and efficient computations are possible owing to the parallel execution of reconfigurable logic *** this study,a dual-logic-in-memory device,which can simultaneously perform two logic operations in four states,is demonstrated using van der Waals ferroelectric field-effect transistors(vdW FeFETs).The proposed dual-logic-in-memory device,which also acts as a twobit storage device,is a single bidirectional polarization-integrated ferroelectric field-effect transistor(BPI-FeFET).It is fabricated by integrating an in-plane vdW ferroelectric semiconductor SnS and an out-of-plane vdW ferroelectric gate dielectric material—CuInP_(2)S_(6).Four reliable resistance states with excellent endurance and retention characteristics were *** two-bit storage mechanism in a BPI-FeFET was analyzed from two perspectives:carrier density and carrier injection controls,which originated from the out-of-plane polarization of the gate dielectric and in-plane polarization of the semiconductor,*** conventional multilevel FeFETs,the proposed BPIFeFET does not require additional pre-examination or erasing steps to switch from/to an intermediate polarization,enabling direct switching between the four memory *** utilize the fabricated BPI-FeFET as a dual-logic-inmemory device,two logical operations were selected(XOR and AND),and their parallel execution was *** types of logic operations could be implemented by selecting different initial states,demonstrating various types of functions required for numerous neural network *** flexibility and efficiency of the proposed dual-logic-in-memory device appear promising in the realization of next-generation low-power computing systems.

关键词： ferroelectric field-effect transistor in-plane ferroelectricity logic-in-memory out-of-plane ferroelectricity

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Implementing a fast unbounded quantum fanout gate using power-law interactions

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Physical Review Research 2022年第4期4卷 L042016-L042016页

作者： Andrew Y. Guo Abhinav Deshpande Su-Kuan Chu Zachary Eldredge Przemyslaw Bienias Dhruv Devulapalli Yuan Su Andrew M. Childs Alexey V. Gorshkov Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

The standard circuit model for quantum computation presumes the ability to directly perform gates between arbitrary pairs of qubits, which is unlikely to be practical for large-scale experiments. Power-law interactions with strength decaying as 1/rα in the distance r provide an experimentally realizable resource for information processing, whilst still retaining long-range connectivity. We leverage the power of these interactions to implement a fast quantum fanout gate with an arbitrary number of targets. Our implementation allows the quantum Fourier transform (QFT) and Shor's algorithm to be performed on a D-dimensional lattice of qubits in time that scales (poly)logarithmically in the number of qubits, using interactions with α≤2D. As a corollary, we show that power-law systems with α≤2D are difficult to simulate classically even for short times, under a standard assumption that factoring is classically intractable. Complementarily, we develop a technique to give a general lower bound—linear in the size of the system—on the time required to implement the QFT and the fanout gate in systems that are constrained by a linear light cone. This allows us to prove an asymptotically tighter lower bound for long-range systems than was possible with previously available techniques.

关键词： Long-range interactions Quantum computation Quantum gates Quantum information processing Quantum protocols

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ChatGPT and Sentiment Analysis: Unveiling Emotional Insights in Textual Data 6

ChatGPT and Sentiment Analysis: Unveiling Emotional Insights...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Suresh Kumar, N. Khan, Zafar Ali Swaminathan, B. Prakash, S. Jayanthi, S. Kumari, Ragini Karnataka Ramanagara India School of Computer Science and Engineering and Information Science Presidency University Karnataka Bengaluru India Department of DS & AI Faculty of Science and Technology The ICFAI Foundation for Higher Education langana Hyderabad India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India

Sentiment analysis plays a critical role in understanding public opinion on social media platforms. This research article presents an in-depth analysis of sentiment in ChatGPT-generated tweets using Natural Language Processing (NLP) techniques which has implications for various domains, including market research, brand reputation management, and public opinion analysis. The objective is to improve the accuracy and effectiveness of sentiment analysis on ChatGPT-generated content. The study begins by pre-processing the tweet data, including the removal of punctuation, special characters, and user mentions. Tokenization is applied to convert the tweets into a structured format while eliminating stopwords to focus on meaningful words. Stemming and lemmatization techniques are employed to further enhance word normalization. Visualizations, such as word clouds, provide insights into the most frequently used words in the ChatGPT tweets, uncovering prevalent topics and themes within the dataset. Sentiment analysis is conducted using Text-Blob and nltk libraries, comparing nltk's Vader Sentiment Intensity Analyzer outperforms TextBlob, achieving an average accuracy of 85% compared to 76%. A machine learning model is constructed using the LinearSVC algorithm, incorporating the Tfidf Vectorizer for feature extraction. The model achieves an accuracy of 89% in sentiment prediction when trained and evaluated on the tweet dataset. To validate the model's performance, an equalized dataset is created, balancing the number of positive, neutral, and negative tweets. It demonstrates consistent accuracy across different sentiment classes, with average accuracies of 87% for positive sentiment, 92% for neutral sentiment, and 86% for negative sentiment. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Emotion Recognition

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Predicting Air Quality in India Using Machine Learning Models 4

Predicting Air Quality in India Using Machine Learning Model...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Swati Kumar, Himanshu Vijarania, Meenu Gautam, Anjali Negi, Neerja Dadhich, Priyanka Kr Mangalam University Department of Computer Science Centre of Excellence Haryana Gurugram India Kr Mangalam University Department of Cse Haryana Gurugram India School of Computer Applications Manav Rachna International Institute of Research & Studies India

ISBN: (纸本)9798350387490

India's poor air quality is responsible for many health concerns, with chronic exposure to fine particulate matter (PM) leading to various health issues. Recently, the pollution levels in India have undergone various changes in the status of pollutants present in the air. Our paper aims to provide a comprehensive analysis of these issues highlighting the severe implications of poor air quality on public health in India and try to predict future health concerns due to poor air quality. In our work we have taken three machine learning modelslinear regression, decision tree regression, and K-Nearest Neighbour into the consideration to train our model. We have evaluated the models by using 'R-squared score' and 'mean square error', to determine which was the best suitable model in predicting the Air quality with better accuracy. Our work contribute to ongoing efforts to improve the air quality forecasts model (AQI) and create effective solutions for a healthier India. © 2024 IEEE.

关键词： Adversarial machine learning

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Aircraft Detection in Satellite Images 1

Aircraft Detection in Satellite Images

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Singh, Abhimanyu Nene, Manisha J. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

Object detection is one of the key areas for all the researchers in the field of computer science. The research is to find the types of objects in the image and provide their temporal and spatial characteristics. In the recent times there have been a lot of improvements in the field of satellite image detection with varying data sets being available and has left high impact on the performance of such analysis. A number of algorithms have evolved over a period of time in object detection and analysis namely different versions of YOLO, CNN, DETR etc. There is a need to deploy a study which enables the performance comparison of different versions of these algorithms on specific data set to understand their efficacy. The study in the paper contributes to understanding, evaluating and analyzing the performance characteristics of YOLO v7 algorithm with varying parameters. © 2022 IEEE.

关键词： Object detection

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Optimization Techniques for Machine Learning Models to Improve the Efficiency of Classification

Optimization Techniques for Machine Learning Models to Impro...

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International Conference on Inventive Computation Technologies (ICICT)

作者： J. Anita Smiles M. Sakthivanitha A. Bharathi D. Narayani. S. Sudha M. Mohamed Sirajudeen Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications Vels institute of Science Technology and Advanced Studies (VISTAS) Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Computer Science PG and Research Nilgiri College of Arts and Science (Autonomous) Thaloor The Nilgiris Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the study is to identify the most optimal set of hyperparameters for a machine learning (ML) or deep learning(DL) algorithms that improves its performance on a certain task.. This study uses five machine learning methods like Decision Tree(DT), Random Forest(RF), Gradient Boost Models(XGBoost), Support Vector Machine(SVMs) and K-Nearest Neighbhor(KNN). The model specific parameters were applied to all these ML methods to improve the accuracy of the models. The ML models performance with its hyperparameter tuning are evaluated for performance using the performance metrics like accuracy, precision, Recall and F-score. These findings indicate that XGBoost models performed significantly in terms of accuracy, precision, recall, and F1-score. Gradient boosting models are extremely adaptable, but they are also sensitive to hyperparameters such as the learning rate, number of estimators, and tree depth. Tuning these parameters can dramatically improve performance. The optimal model and tuning method are determined by the dataset, task specifications, and computing power.. The contribution of the study to suggests a suitable with right hyperparameter settings to develop a highly flexible model that can adapt to a variety of datasets. The study and application of model-specific hyperparameters in ML continues to evolve, resulting to advances that improve productivity, durability, and generalization.

关键词： Support vector machines Adaptation models Accuracy Machine learning algorithms Computational modeling Forestry Vectors Decision trees Tuning Random forests

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FMCW Monopulse Radar with an Orthogonally Polarized Dual-Beam Dielectric Resonator Antenna

FMCW Monopulse Radar with an Orthogonally Polarized Dual-Bea...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Lee, Chanhee Shin, Kyeong-Han Lee, Ji-Hoon Lee, Hyo-Won Kim, Jeong-Wook Yu, Jong-Won School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Electrical and Computer Engineering Ajou University Suwon Korea Republic of

ISBN: (纸本)9798350364774

In this paper, frequency-modulated continuous-wave (FMCW) radar system using dielectric resonator antenna (DRA), which has two beams with orthogonal linear polarization to each other, is introduced to function as a monopulse radar. The DRA operates at 24.3 GHz with a 16.8% bandwidth and provides over 40 dB isolation between V-pol and H-pol ports. Simulations demonstrate effective target detection and advanced directional information extraction, similar to monopulse radar, using a signal processing technique to address overlapping beam regions. © 2024 IEEE.

关键词： Monopulse radar

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nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering 7

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

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