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Air quality and dust level monitoring systems in hospitals using IoT

Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Intelligent Crop Recommender System for Yield Prediction Using Machine Learning Strategy

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 979-987页

作者： Maheswary, Atchukatla Nagendram, Sanam Kiran, Kasi Uday Ahammad, Shaik Hasane Priya, Putcha Poorna Hossain, Md. Amzad Rashed, Ahmed Nabih Zaki Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering & ampTechnology Thirupatchur India Department of Artificial Intelligence KKR & ampKSR Institute of Technology and Sciences Guntur India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur522502 India Department of Electronics and Communication Engineering Dadi Institute of Engineering and Technology College Anakapalle India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Saveetha University Tamilnadu Chennai India

For most developed nations, agriculture is a significant economic force. The realm of contemporary agriculture is consistently growing with evolving farming techniques and agricultural innovations. Farmers face challenges in keeping pace with the evolving demands of the planet and meeting the requirements of profitable initiatives, characters, and various other stakeholders. Climate change brought on by industry emissions and soil erosion, soil's nutrient deficiency due to mineral's absence, which results in reduced crop growth, and the cultivation of the same crops repeatedly without trying out new varieties are a few of the difficulties farmers face. Without considering the lower quality or quantity, they arbitrarily infuse fertilizers. Using two separate metrics, entropy and Gini indexes, the study analyzes well-known procedures with K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classifier practices. Moreover, the precision under the agriculture paradigm, particularly "crop recommender systems," includes these methods. Based on the outcomes, the random forest strategy outperforms the other approaches to model accuracy and reliability. © The Institution of Engineers (India) 2024.

关键词： Fertilizers

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer N

关键词： Molecules

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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VTAF-Ensemble: A Hybrid Vision Transformer and Attention-based Learning Approach for Enhanced Brain Tumor Detection and Classification 1

VTAF-Ensemble: A Hybrid Vision Transformer and Attention-bas...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Fathima, Mohammed Nazneen Singh, Prabhjot Singh, Simrandeep Chandigarh University Department of Electronic and Communication Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331529246

This work introduces a new methodology, Vision Transformer with Attention-based Feature Mapping and Optimized Ensemble Learning (VTAF-Ensemble), which includes three advanced techniques: Vision Transformer-Based Segmentation (VT-Seg), Attention-Based Feature Mapping (AF-Map) and Optimized Ensemble Learning (OE-Ensemble), to boost the brain tumor detection and classification from Magnetic Resonance Imaging (MRI) images. Traditional practice of identifying tumor is error prone, time consuming, hence it requires automation. This thesis proposes an integrated method, which includes VT Seg for accurate segmentation, AF Map for feature enhancement, and OE Ensemble for Ensemble classification using SVM and CNN classifiers. We show that VTAF-Ensemble achieves 92.1% Intersection over Union (IoU), 94.5% Mean Average Precision (mAP), 93.2% Dice Coefficient (DC) and 95% sensitivity in segmentation. Moreover, the model also yields better results on classification metrics compared to others: Area Under the Curve (AUC) of 95.2% and F1 Score of 92.9% and recall of 93%. Overall, this hybrid approach provides substantial accuracy improvements, scalability and utility for medical image applications as they relate to medical imaging applications, in particular, brain tumor specific diagnostic applications. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Compact Wideband Antenna Array with DGS-Based Metamaterial for Efficient Smartphone communication and SAR Reduction

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Progress in Electromagnetics Research B 2025年 110卷 15-28页

作者： Megahed, Amany A. Hussein, Amr H. Al-Gburi, Ahmed Jamal Abdullah Elabd, Rania Hamdy Electronic and Communication Department Higher Institute of Engineering and Technology in New Damietta Damietta34517 Egypt Electronics and Electrical Communications Engineering Department Faculty of Engineering Tanta University Egypt Malacca Malaysia Electronics and Communications Engineering Department Faculty of Engineering Horus University Egypt New Damietta Egypt

This study investigates a high-gain, miniaturized antenna array featuring semicircular Defected Ground Structures (DGSs) based metamaterial designed for wideband smartphone applications. The antenna array, measuring 49 × 25 mm2 is constructed on an FR4 substrate with a dielectric constant of 4.3 and a thickness of 1.6 mm. The design incorporates two orthogonal antennas, each with a U-shaped radiating patch and a semicircular DGS to control bandwidth and reduce size. A T-shaped stub is positioned at the center of the U-shaped radiating area, with a star-shaped element attached to the leg of the T-shaped stub to enable wideband operation. The antenna demonstrates strong S11 performance, achieving approximately −38 dB at 5.8 GHz and −42 dB at 8.1 GHz, making it ideal for Sub-6 GHz and C-band applications. The proposed antenna array operates across a frequency range from 4 GHz to beyond 10 GHz, reaching a peak gain of 11 dBi and an efficiency of 95%. A time-domain analysis was conducted to verify radiation efficiency, and the specific absorption rate (SAR) is approximately 0.0475 for 1 g of tissue and 0.0101 for 10 g of tissue at 4.5 GHz, confirming the array's suitability for wideband smartphone devices within the target frequency band. The simulated and experimental results of the proposed antenna array show excellent agreement. © (2025), (Electromagnetics Academy). All rights reserved.

关键词： Tissue

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Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

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computers, Materials & Continua 2023年第6期75卷 5193-5211页

作者： Han Yanyan Wei Wanqi Dou Kaili Li Peng College of Information Engineering Xidian UniversityXi’an710071China Department of Electronic and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing100070China Department of Cyberspace Security Beijing Electronic Science and Technology InstituteBeijing100070China

As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this *** of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double *** the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital *** on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’*** sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other *** the upload test,the average upload speed reaches 80.6 M/*** average download speed of the system is 88.7 M/s after the download *** multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 *** system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.

关键词： Dual blockchain credit data sharing truffle framework digital envelope ipfs

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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer Science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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computer Modeling in engineering & Sciences 2024年第8期140卷 1689-1710页

作者： Md.Abdur Rahim Fahmid Al Farid Abu Saleh Musa Miah Arpa Kar Puza Md.Nur Alam Md.Najmul Hossain Sarina Mansor Hezerul Abdul Karim Department of Computer Science and Engineering Pabna University of Science and TechnologyPabna6600Bangladesh Faculty of Engineering Multimedia UniversityCyberjaya63100Malaysia Department of Computer Science and Engineering Bangladesh Army University of Science and TechnologySaidpur5311Bangladesh Department of Mathematics Pabna University of Science and TechnologyPabna6600Bangladesh Department of Electrical Electronic and Communication EngineeringPabnaUniversity of Science and TechnologyPabna6600Bangladesh Department of Electrical and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Handwriting is a unique and significant human feature that distinguishes them from one *** are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through ***,such systems are susceptible to forgery,posing security *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or *** innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and *** key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive ***-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite *** meticulous amalgamation resulted in a robust set of 91 *** enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent *** the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting ***,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual ***,our experimental results unde

关键词： Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM

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Design home security using artificial intelligence 1st

Design home security using artificial intelligence

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Gautam, Nikhil Mojahid, Md. Murari, Krishna Sazid, Mohd Department of Electronic and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

The advent of artificial intelligence has revolutionized various aspects of daily life, including home security systems. In this paper, we present a novel approach to designing a comprehensive home security system using Artificial intelligence technology. The system integrates various components such as Arduino Uno -ESP32 for data transmission, AWS IoT Core for cloud connectivity, a solenoid lock, and Wi-Fi modules for internet connectivity. Additionally, the system incorporates AI algorithms to monitor and control household devices like lights and fan, enhancing both security and energy efficiency. A key highlight of our approach is the use of AI- powered smart door locks, key, which distinguish between authorized and unauthorized individuals. This paper discusses the design, implementation, and evaluation of our system, highlighting its effectiveness in providing robust home security while minimizing false alarms. New home security systems consolidate with keen home advanced help. Lights can partner with self-observed home security system, turning on a proprietor’s lights in the event of a break-in. Things like Amazon’s Alexa, Apple’s Siri, or Google Assistant are additionally adequate with different alarms and security associations. © 2025 the Author(s).

关键词： Wi-Fi

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