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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching

Towards Efficient Deep Hashing Retrieval: Condensing Your Da...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Tao Zhang, Jie Liu, Huashan Wang, Zhijie Pang, Shengyuan College of Information Science and Technology Donghua University Shanghai China Department of Computer Science ETH Zurich Zurich Switzerland College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing models has become more expensive due to complex optimizations and large datasets. Coreset selection and Dataset Condensation lower overall training costs by reducing the volume of training data without significantly compromising model accuracy for classification task. In this paper, we explore the effect of mainstream dataset condensation methods for deep hashing retrieval and propose IEM (information-intensive feature-Embedding Matching), which is centered on distribution matching and incorporates model and data augmentation techniques to further enhance the feature of hashing space. Extensive experiments demonstrate the superior performance and efficiency of our approach. © 2025 IEEE.

关键词： Dataset Condensation Deep Hashing Retrieval

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Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

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computer Systems science & engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System Design Chennai Institute of TechnologyChennai600069India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3D CNN brain tumor

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IoT-Based Smart Mask Protection against the Waves of COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11153-11164页

作者： Goar, Vishal Sharma, Aditi Yadav, Nagendra Singh Chowdhury, Subrata Hu, Yu-Chen Govt. Engineering College Bikaner Rajasthan Bikaner India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India Department of Computer Science & Applications SVCET Engineering College Andra Pradesh Chittoor India Department of Computer Science and Information Management Providence University 200 Sec. 7 Boulevard Shalu Dist Taichung City43301 Taiwan

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytic

关键词： COVID-19

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Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

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computer Systems science & engineering 2023年第2期44卷 961-977页

作者： E.Dhiravidachelvi M.Suresh Kumar L.D.Vijay Anand D.Pritima Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Electronics and Communication Engineering Mohamed Sathak A.J.College of EngineeringChennai603103India Department of Information Technology Sri Sairam Engineering CollegeChennai602109India Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatore641114India Department of Mechatronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artiﬁcial Intelligence(AI)*** techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and *** wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor ***,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,***-developed Artiﬁcial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR *** this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare *** proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare *** addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the ***-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of ***,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN ***,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different *** experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

关键词： Artiﬁcial intelligence human activity recognition deep learning deep belief network hyperparameter tuning healthcare

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MagneComm+: Near-Field Electromagnetic Induction Communication With Magnetometer

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IEEE Transactions on Mobile Computing 2023年第5期22卷 2789-2801页

作者： Xue, Guangtao Pan, Hao Chen, Yi-Chao Ji, Xiaoyu Yu, Jiadi Shanghai Jiao Tong University College of Computer Science and Technology Shanghai200240 China Zhejiang University School of Electronic Information and Electrical Engineering Zhejiang Hangzhou310027 China

Near-field communication (NFC) technology emerges as a vital role with appealing benefits for users to improve mobile device's functionality. Although today's most smartphones and smartwatches come with NFC support, other mobile devices (e.g., PC and laptops) and IoT devices that don't equip with dedicated radio modules cannot take advantage of wide-scale NFC capability. We design and develop MagneComm+, an NFC-like implementation scheme without dedicated hardware and propose a novel near-field communication protocol that is applicable to almost all mobile devices and IoT devices. The key idea is to utilize the electromagnetic induction (EMI) signal emitted from the computing devices (e.g., CPUs) and captured by magnetometers on mobile devices for communication. We tackle challenges in data encoding/decoding, preamble detection, retransmission and error correction, multi-transmitter, and full-duplex schemes, to efficiently generate and reliably receive EMI signal with the hardware available on devices. We prototype MagneComm+ on both between laptops and smartphones, as well as between two laptops with an external magnetometer. Extensive evaluation results show that our MagneComm+ supports around 10∼;cm10cm communication distance with average 110 bps(bit per second) data rate on the normal-speed mode, and maximum 17.28 kbps on the full-speed mode. © 2002-2012 IEEE.

关键词： Near field communication

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Mimo Systems with Improved Channel State Infrmations with ML and AI

Mimo Systems with Improved Channel State Infrmations with ML...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Thiruvenkadam, T. Hassan, Inzimam Ul Agarwal, Trapty Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

The utilization of huge various information different result (MIMO) millimeter-wave (mmWave) frameworks is presently viewed as an expected choice for the improvement of fifth-age (5G) mobile phone organizations. Anyway, one major challenge in this field is to the high power consumption resulting from the many radio frequency (RF) links. By using not high-quality analog-to-digital converters (ADCs), this problem may be solved and a fair trade-off between performance and power efficiency can be made. This discussion investigates numerous significant points roused by the latest discoveries on mmWave immense MIMO frameworks utilizing low-goal ADCs. Our examination centers around significant issues connected with handset configuration, including channel gauges, signal distinguishing proof, criticism on channel data, and communicate precoding. Moreover, a clever methodology is presented looking like a blended ADC development, giving an alternate way to work on the general execution of the framework. Finally, we explore the complexities of deploying realistic 5G mmWave large MIMO systems with ADC quantizers, discuss related issues, and consider possible practical uses. Without using plagiarism, this synthesis seeks to provide a thorough summary of the relevant concerns surrounding this state-of-the-art technology. © 2023 IEEE.

关键词： channel assessment Channel State information (CSI) enormous MIMO low-goal ADCs mixed-ADC architecture mmWave signal detection Signal-to-Noise Ratios (SNRs) and 5G networks transmit precoding

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A Comparative Analysis of Three Multispectral Images in Classification with Different Methods 24

A Comparative Analysis of Three Multispectral Images in Clas...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Chen, Feng Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Jiangxi Key Laboratory of Watershed Soil and Water Conservation Jiangxi Academy of Water Science and Engineering Nanchang China

ISBN: (纸本)9798400717482

Benefited from the increased satellite observations with medium spatial resolution, land use/cover mapping at local to global scales has provided important supports for society and academy community. For specific purposes (e.g., disaster assessment, detecting surface abrupt change), it is needed to increase the observation frequency, through combining different satellite sensors (i.e. as virtual constellation). However, many factors challenge this combination in practice, in which the comparability issue in sensor settings should be considered among various payload platforms. In this study, from the perspective of practical application, a comparative analysis of land use/cover mapping was investigated, with focusing on three observations by the Gaofen-1 (GF-1) wide field of view (WFV), Sentinel-2A Multi-Spectral Instrument (MSI), and Landsat-8 Operational Land Imager (OLI) respectively. Four methods were implemented in classification experiments. Preliminary results showed that the Sentinal-2A MSI outperformed the GF-1 WFV and the Landsat-8 OLI in classification, no matter which classifier was considered. Meanwhile, the higher spatial resolution of the GF-1 WFV certainly compensated its lack of spectral channels compared against the Landsat-8 OLI. The choice of methods affected classification for a specific observation, while the more suitable classifier varied among sensors. This study underlines the compatibility issue in land use/cover mapping with different observations. © 2024 Copyright held by the owner/author(s).

关键词： Mapping

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Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

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Digital Communications and Networks 2023年第5期9卷 1131-1140页

作者： Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Department of Computer Science COMSATS University IslamabadPakistan Department of Information Technology Quaid-e-Azam University IslamabadPakistan College of Science and Engineering School of Computing and MathsUniversity of DerbyDE221GBUK Department of Embedded Systems Engineering Incheon National UniversitySouth Korea

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not *** the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple *** this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like ***-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive *** demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri *** results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

关键词： Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p,l)-Angelization QT HLPN

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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