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Smartphone-Integrated Optomechanical Dual-Mode Instrument for Salmonella Typhimurium Detection

IEEE Sensors Journal 2025年第11期25卷 20423-20432页

作者： Min, Hyun Jung Mina, Hansel A. Oh, Junseok Deering, Amanda J. Paul Robinson, J. Rajwa, Bartek Bae, Euiwon BeltsvilleMD20705 United States Purdue University Department of Food Science West LafayetteIN47906 United States Purdue University Department of Computer Science West LafayetteIN47906 United States Purdue University Department of Basic Medical Sciences School of Biomedical Engineering West LafayetteIN47906 United States Purdue University Bindley Bioscience Center West LafayetteIN47906 United States Purdue University School of Mechanical Engineering West LafayetteIN47906 United States

Salmonella typhimurium is responsible for numerous cases of foodborne illnesses, often resulting in severe hospitalizations and fatalities. The imperative to detect and prevent such foodborne pathogens is crucial for safeguarding public health. This research introduces the creation of a quartz-crystal microbalance (QCM) system integrated with a smartphone, aiming to identify the presence of S. typhimurium through a dual-mode approach. This innovative system employs two distinct mechanisms: first, by gauging frequency changes induced by bacterial mass, and second, by quantifying fluorescence intensities arising from bacteria captured by fluorescein isothiocyanate (FITC)-labeled antibodies. The utilization of FITC-labeled antibodies impacts the measurement in two ways: it enhances the frequency-shift signal for mass change and facilitates the visualization of bacterial cells via optical detection. The integration of the smartphone with the QCM system enables the real-time presentation of frequency data and the recording of fluorescence intensities, allowing for the estimation of cell numbers. The smartphone-based system effectively detected S. typhimurium within a concentration range of 103 – 105 CFU/mL following the application of FITC-labeled antibodies. © 2001-2012 IEEE.

关键词： Quartz crystal microbalances

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Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

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computer Systems Science & engineering 2023年第8期46卷 1431-1445页

作者： A.Francis Saviour Devaraj T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia Mohamad Adzhar Md Zawawi Mohamad Khairi Ishak Department of IT The University of Technology and Applied Sciences-IbriPO Box:466IbriPostal Code:516Sultanate of Oman Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains MalaysiaNibong Tebal14300PenangMalaysia

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing *** article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for *** presented EMTA-SRSP technique majorly involves the optimal selection of routes in *** accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data *** the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust *** experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

关键词： Security wireless sensor networks trust factor routing protocol privacy

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Securing Health Monitoring in IOT with Blockchain-Based Proxy Re-Encryption

Securing Health Monitoring in IOT with Blockchain-Based Prox...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Jerlin George R. Chitra Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

The devices with sensors that can monitor your temperature, heart rate, and other vital signs and link to the internet, known as the Internet of Things (IoT), have completely transformed the way we control health. Providing real time health data, these sensors improve diagnostics and treatment outcomes. Security and privacy matters when IoT comes into play in healthcare. Cyberattacks on centralized database systems are also a problem. To solve these challenges, the study uses blockchain technology coupled with proxy re-encryption to secure health data. ThingSpeak IoT cloud analyzes the collected data and turns them into blockchain transactions which are safely kept on the DriveHQ cloud. Transparency and data integrity are ensured by blockchain, and secure data sharing among authorized users is made possible by proxy re-encryption. This results in a health monitoring system that preserves the accuracy and confidentiality of data while reducing the safety risks of IoT driven healthcare applications.

关键词： Temperature sensors Temperature measurement Cloud computing Medical services Blockchains Temperature control Safety Internet of Things Security Monitoring

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An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

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computers, Materials & Continua 2023年第3期74卷 5821-5836页

作者： Ala Saleh Alluhaidan Pushparaj Anitha Subbappa Ved Prakash Mishra P.V.Chandrika Anurika Vaish Sarthak Sengupta Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhKSA.P.O.Box:8442811671 Department of Electronics and Communication Engineering National Institute of Technical Teacher Training and ResearchChandigarh160019India Department of Periodontology JSS Dental College&HospitalJSS Academy of Higher Education and ResearchMysuru570015India Department of Computer Science and Engineering Amity UniversityDubai345019United Arab Emirates Department of Management/Economics Prin LN Welingkar Institute of Management Development and ResearchMumbai400019India Department of Management Studies Indian Institute of Information TechnologyAllahabad211012India

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt *** still have been pioneering collections of massive volumes of health *** detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts *** linkage specifies the issue of finding identical records across various data *** similarity existing between two records is characterized based on domain-based similarity functions over different ***-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining *** objective is to match all the records associated with the same *** measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been *** outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is *** article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of *** first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)*** the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective ***,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

关键词： Duplicate detection healthcare record linkage dataset pre-processing reciprocal neuro-fuzzy inference system and ant lion optimization fuzzy system

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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Adaptive Fine-Grained Feature Mining and RoI Feature Interaction Network for Small Object Detection in Aerial Images

Adaptive Fine-Grained Feature Mining and RoI Feature Interac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Fangmin Xie Zhan Xiong Zhuoyue Wang Kun Yang Guoqiang Xiao College of Computer and Information Science Southwest University China Institute of Advanced Computer Science Leiden University Netherlands Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects. To address these issues, we propose a small object detector called Adaptive Fine-grained Feature Mining Network (AFMNet) based on an anchor-free framework. Firstly, an Adaptive Fine-grained Feature Mining (AFFM) module is designed to generate Regions of Interest (RoI) on high-resolution feature maps and mine fine-grained information that benefits the representation of small objects. Secondly, an Inter-RoI Feature Interaction (IRFI) block is developed to efficiently integrate complementary cues— namely, deep layer semantic information and shallow layer spatial details—and enhance RoI features. Experiments on the VisDrone dataset demonstrate that our method achieves state-of-the-art performance. The source code of our AFMNet is available at https://***/xiefangmin/AFMNet.

关键词： Adaptive systems Source coding Semantics Lighting Object detection Detectors Signal processing Feature extraction Speech processing Drones

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Breast Cancer Classification Using Deep Neural Networks Technique

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Procedia computer Science 2025年 258卷 183-192页

作者： Sachin B. Jadhav Pratik Pal Vijay R. Ghorpade Ramesh T.Patil School of Computer and Systems Sciences(SC&SS) Jawaharlal Nehru University(JNU) New Delhi-110067 India Department of Computer Science Engineering Bharati Vidyapeeth College of Engineering Kolhapur-416013 India Department of Electronics and Telecommunication Engineering Rajarambapu Institute of Technology Islampur-415414 India

Breast cancer is one of the important reasons for the demise of women globally. Precise and initial recognition of breast cancer can confirm lasting survival for the patients. Deep neural network-based techniques can assist pathologists and doctors in precisely detecting abnormalities. The efficacy of our proposed artificial deep neural network A-DNN model is tested on Breast Cancer data sets (Wisconsin Diagnostic-WDBC), and the outcomes demonstrate that the A-DNN model outstrips other evaluation approaches. The key objective of this research work was to apply a proposed approach in an actual medical investigative system and thus assist medical doctors in making exact and actual decisions in the future. The A-DNN model was studied in experimental work at numerous training-testing partitions of the WDBC data set. The A-DNN model obtains a micro average classification accuracy of 99%, precision of 99%, and recall of 99% on the 70-30% partitioning of a WDBC dataset. The A-DNN classifier model gives an accuracy of 99% representing promising results compared to the other research study.

关键词： Breast cancer classification Deep neural network Deep learning Adam Optimization

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Mining High Utility-probability Sequential Patterns in Bigdata Environments 14th

Mining High Utility-probability Sequential Patterns in Bigd...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Wu, Jimmy Ming-Tai Liu, Shuo Wu, Mu-En Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China National Taipei University of Technology Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

ISBN: (纸本)9789811684296

High utility-probability sequential pattern mining (HUPSPM) has become a novel research errand realm in data mining. In practical application, most of the data are collected by a variety of sensors, forming an uncertain database. Therefore, the data collected in real life can be expressed in terms of probability. In previous research work, for the sake of heightening the performance of mining in small data sets and single machine, a great deal of algorithm have been posed. Unfortunately, these algorithms have loopholes in mining large databases with mickle records. Therefore, it is very urgent to construct an efficient model to deal with large uncertain dateabas data sets. In this article, a MapReduce framework is moved to mine effective information in multitudinous uncertain data sets and reduce the number of database scans. Experiments on a large number of uncertain data sets show that the posed Hadoop algorithm has a strong ability to mine the high utility-probability sequential pattern. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probability

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Real-Time Speech Emotion Recognition Using Hybrid Transformer Networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Durga, P. Janani Basha, C. H. Hussaian Christopher, Nelson Kennedy Babu Senthilkumar, S. Kumar, N. Rathina Kavitha, M. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Sr University Department of Electrical and Electronics Engineering Telangana Hanumakonda50637 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Sns College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Coimbatore641107 India

ISBN: (纸本)9798331509828

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. Data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation. © 2025 IEEE.

关键词： Transfer learning

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Efficient Adaptive Feedback Cancellation Method for Digital Hearing Aids Employing Short Processing Delays

Efficient Adaptive Feedback Cancellation Method for Digital ...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Muhammad Tahir Akhtar Department of Electrical and Computer Engineering School of Engineering and Digital Sciences Nazarbayev University Astana Republic of Kazakhstan

ISBN: (数字)9798350361759

ISBN: (纸本)9798350361766

The presence of acoustic feedback in digital hearing aids is almost an unavoidable phenomena, as the input microphone and loudspeaker are located very close to each other. The presence of strong acoustic feedback may result in howling effect, which degrades the performance of hearing aid. This paper presents an efficient solution for mitigating acoustic feedback in modern hearing aids equipped with a very short processing delays. The developed method employs two adaptive filters (AFs) and a delay-based strategy to monitor their convergence. The hearing aid received (amplified) signal acts as the input to the main adaptive filter (AF) which is adapted using lattice filtering-based algorithm with improved decorrelation properties. An internally generated random probe signal is input to the auxiliary AF which is adapted using a hybrid adaptive algorithm combining feature of normalized least mean square (NLMS) and maximum Versoria-criterion (MVC) cost functions. The computer simulation demonstrate that the proposed is quite efficient in mitigating the effect of the acoustic feedback in comparison with the other counter parts.

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