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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Puthal, Deepak Mohanty, Saraju P. Yeun, Chan Yeob Damiani, Ernesto Pradhan, Biswajeet University of North Texas Department of Computer Science and Engineering DentonTX United States Khalifa University Center for C2PS Department of Eecs Abu Dhabi United Arab Emirates Università Degli Studi di Milano Department of Computer Science Milan Italy University of Technology Faculty of Engineering and It Sydney Australia

ISBN: (纸本)9798350330014

The proliferation of Internet of Things (IoT) devices has led to an explosion of data generated at the edge of the network. This has led to the development of the IoT -Edge-Cloud continuum, where data is processed and analyzed across multiple layers of the network. However, this also poses significant security and privacy challenges, as sensitive data is transmitted across the network. In this paper, we propose the use of Pervasive Artificial Intelligence (AI) to enhance the security and efficiency of the IoT -Edge-Cloud continuum. Pervasive AI can be used at each layer of the network to process and analyze data in real-time, reducing the amount of data that needs to be transmitted across the network. This can help to improve response times, reduce network traffic, and enhance security by keeping data localized. We also discuss the potential cybersecurity threats and future research directions for the use of Pervasive AI in the IoT - Edge-Cloud continuum. Our findings suggest that Pervasive AI has significant potential to enhance the security and efficiency of the IoT -Edge-Cloud continuum, and should be considered as a viable solution for future IoT deployments. © 2023 IEEE.

关键词： Internet of things

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Stationary Vehicles Detection on Smart Highways and Roads using Spatio-temporal Tracking 16

Stationary Vehicles Detection on Smart Highways and Roads us...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Khan, Wasiq Kelly, Jessica Al-Kafri, Ala Kleanthous, Natasa Khayam, Umar Khan, Bilal Liverpool John Moores University Faculty of Engineering & Technology Liverpool United Kingdom Teesside University School of Computing Engineering and Digital Technologies United Kingdom Khalifa University Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Comsats University Pakistan California State University School of Computer Science & Eng San BernardinoCA United States

ISBN: (纸本)9798350381344

Where traditional motorways contain hard shoulders to provide refuge for broken-down vehicles, smart motorways instead use live outer lane to ease congestion. The live lanes can be closed due to accidents or breakdowns which is communicated to other road users through overhead gantry signs. This can only occur if the traffic management control center is made aware of the stationary vehicle(s), through either notification via phone call or Motorway Incident Detection and Automatic Signaling (MIDAS) induction loop technology. Alternatively, radar-based stopped vehicle detection is used to identify non-moving objects on the highway. However, this technology is unable to recognize objects or distinguish between congestion and break down etc., which leads to generate false alarms. For the first time, we propose a fully autonomous computer vision and deep learning-based solution to detect stationary vehicles on highways and local roads. We employ deep transfer learning to build a custom-trained vehicle detection model using a newly prepared dataset comprising over 105,000 annotated vehicle instances. DeepSort algorithm is employed for real-time vehicle tracking through associating instances between time series frames, followed by a rule-based algorithm to identify the current state of detected vehicles. Experimental outcomes show our approach as outperforming the state-of-the-art methods in terms of efficient and reliable detection of stationary vehicles (with 98.3% accuracy) as well as distinguish them from congestions when evaluated over video streams captured in realistic dynamic and diverse conditions. © 2023 IEEE.

关键词： Vehicles

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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IoT-driven Smart Packaging for Pharmaceuticals: Ensuring Product Integrity and Patient Safety 1

IoT-driven Smart Packaging for Pharmaceuticals: Ensuring Pro...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Raman, Ramakrishnan Dhivya, K. Sapra, Pooja Gurpur, Shashikala Maniraj, S.P. Murugan, S. Maharashtra Pune India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Maharashtra Pune India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

In this paper, an IoT-driven smart packaging in the pharmaceutical industry is developed to maintain product integrity and improve patient safety. To monitor essential environmental and handling conditions, the research incorporates several sensors into pharmaceutical packaging, including temperature, humidity, light, tamper detection, gas, GPS, accelerometer, RFID tags, moisture indicator sensors, and shock/vibration sensors. These sensors monitor temperature, humidity, light, tamper detection, and gas. IoT-enabled smart packaging may proactively detect deviations from ideal storage and transportation conditions, decrease risks of product deterioration, and prevent tampering by collecting and analyzing data in real-time. This enables smart packaging to avoid tampering. The deployment of such intelligent packaging technologies can revolutionize medication management, improve patient outcomes by assuring the delivery of effective and legitimate pharmaceuticals, and boost supply chain visibility. This study highlights the opportunities and obstacles of implementing IoT-driven smart packaging in the pharmaceutical business. As a result, the research paves the way for better and safer healthcare practices. © 2023 IEEE.

关键词： Healthcare Applications Real-time Monitoring Smart Packaging Supply Chain Management Wireless Communication

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Automatic Speech Recognition Techniques for Transcription of Thai Traditional Medicine Texts

Automatic Speech Recognition Techniques for Transcription of...

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International SoC design Conference, ISOCC

作者： Jettasic Popun Wilaiporn Lee Akara Prayote Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

Currently, Automatic Speech Recognition (ASR) technology is widely used for communication convenience in converting speech to text. Initial surveys reveal that existing studies on ASR, both in Thai and foreign languages, focus on contemporary language communication, lacking support for specialized vocabulary. Traditional Thai Medicine contain ancient medical terminology recorded in various media such as palm leaves, parchment, notebooks, and inscriptions. Centuries of textual preservation have led to text degradation. Given the suitability of ASR study to accommodate medical terminology and ancient languages, it is proposed to apply it to convert original Traditional Thai Medicine texts into contemporary language, aiding experts in reading and interpreting them. ASR involves converting speech signals into text. In this study, Hidden Markov Model techniques using Kaldi are employed. Due to the absence of a Thai Traditional Medicine vocabulary repository, a dataset comprising text and speech is developed, consisting of 900 training sentences and 100 test sentences randomly selected from recordings of 10 Thai Traditional Medicine experts. Offline evaluation methods are employed, resulting in improved accuracy rates. Future efforts will focus on expanding the speech dataset to enhance recognition accuracy further. This study lays the groundwork for efficiently extracting knowledge from Traditional Thai Medicine prescriptions, ensuring the preservation of this valuable heritage for future generations.

关键词： Training Surveys Vocabulary Accuracy Hidden Markov models Prototypes Speech enhancement Recording Automatic speech recognition Speech to text

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High Time Resolution, Low-Noise, Power-Efficient, Charge-Sensitive Amplifier in 40 nm technology 31

High Time Resolution, Low-Noise, Power-Efficient, Charge-Sen...

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31st International Scientific Conference Electronics, ET 2022

作者： Zaki, Alireza Mohammad Nihtianov, Stoyan Delft University of Technology Mekelweg Faculty of Electrical Engineering Mathematics and Computer Science Department of Microelectronics Mekelweg 5 Delft2628 Netherlands

ISBN: (数字)9781665498784

ISBN: (纸本)9781665498784

Particle detection circuits are used for a wide range of applications from experimental physics to material testing and medical imaging. In the state-of-The-Art systems, the trend is to design low-noise and low-power readout front-end electronics with a low detection error rate and small silicon area occupation. This paper presents the design of a high time resolution, low-noise, and power-efficient charge sensitive amplifier (CSA) in 40 nm CMOS technology. For every charge pulse of the detector, the CSA generates voltage signals with a peak amplitude of 30.6 mV, a rise time of 2.35 ns, and an equivalent noise charge (ENC) of 44e- with 0.14 mW power consumption. © 2022 IEEE.

关键词： Medical imaging

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Analysis and Optimization of Neural Networks for Image and Speech Recognition

Analysis and Optimization of Neural Networks for Image and S...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Komala, C.R. Purushothaman, R. Ande, Pavan Kumar Dinesh, P.M. Anitha Rani, K.S. Yamuna, P. HKBK College of Engineering Department of Information Science and Engineering Karnataka Bengaluru560045 India Sri Ramachandra Institute of Higher Education and Research Faculty of Engineering and Technology Tamil Nadu Chennai600116 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem636005 India REVA University School of Computer Science and Applications Karnataka Bengaluru560064 India Acharya Institute of Graduate Studies Department of Computer Applications Karnataka Soladevanahalli 560107 India

ISBN: (纸本)9798350319200

The strategy of transforming vocal discourse signals into text using records is known as programmed discourse acknowledgment (ASR). In the ongoing time of the PC unrest, the ASR has a huge impact in further developing the client experience while communicating with the PCs in a characteristic manner. Conventional instruments like a console and mouse can't be utilized, and the client might do a limitless number of undertakings like controlling of contraptions and correspondence with client care. This study presents an air terminal enquiry framework in view of ASR. The program was made solely for the Telugu language. In view of the most generally posed inquiries about air terminals, a data set was created. Convolutional Brain Organization (CNN) has been utilized for preparing and testing the information base in view of its extraordinary exhibition. Convolution brain organizations (CNNs) have as of late been demonstrated to perform better on the telephone discovery test than regular completely associated profound brain networks when utilized in a half and half profound brain organization/stowed away Markov model (DNN/Gee) design. © 2023 IEEE.

关键词： Image Networks Neural Optimization Speech Recognition

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Multi-Agent System Based on Stacking Technique for Rice Yield Prediction

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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Electronic engineering (ICEEM), International Conference on

作者： Hayam R. Seireg Yasser M. Omar Ahmed Elmahalawy Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Computer Science Faculty of Computing and Information Technology AASTMT Cairo Egypt

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA.

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Large Eddy Simulations of excited flames issuing from rectangular nozzles 25

Large Eddy Simulations of excited flames issuing from rectan...

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25th Fluid Mechanics Conference, FMC 2022

作者： Stempka, Jakub Tyliszczak, Artur Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology Al. Armii Krajowej 21 Czestochowa42-201 Poland

In the current study we assess an impact of passive and active flow control methods applied to the flames emanating from rectangular nozzles. The passive control is realised by varying a jet aspect ratio using differently shaped nozzles, while the active control relies on applying a forcing to the axial component of velocity. The analysis is performed through a series of high fidelity Large Eddy Simulations (LES) using a high order in-house solver. Combustion process is modelled using the detailed chemistry model of a hydrogen oxidation with a corresponding set of species transport equations. It is shown that the passive flow control affects the jet flames significantly only up to a moderate forcing frequency. For a high frequency excitation the effect of the aspect ratio is weak. A combination of both control methods is found as very efficient in altering important practical flame features like a lift-off height and flame shape. © Published under licence by IOP Publishing Ltd.

关键词： Aspect ratio

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Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

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computers, Materials & Continua 2023年第6期75卷 5213-5228页

作者： Sobia Nawaz Sidra Rasheed Wania Sami Lal Hussain Amjad Aldweesh Elsayed Tag eldin Umair Ahmad Salaria Mohammad Shahbaz Khan Basic Health Unit Sumra Lodhran59320Pakistan Basic Health Unit 20-G ChishtianBahawalnagar62300Pakistan Doctor’s Hospital Lahore54590Pakistan Department of Computer Science&IT Neelum CampusThe University of Azad Jammu and KashmirAthmuqam13230Azad KashmirPakistan Department of Computer Science&IT King Abdullah CampusThe University of Azad Jammu and KashmirMuzaffarabad13100Azad KashmirPakistan College of Computer science and information technology Shaqra UniversityShaqra15273Saudi Arabia Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Department of Electrical Engineering University of Azad Jammu and KashmirChehla CampusMuzaffarabad13100Azad KashmirPakistan Department of Electrical Engineering Mirpur University of Science&TechnologyMirpurMuzaffarabad10250Azad KashmirPakistan Children’s National Hospital 111 Michigan AVE NWWashingtonDC20854USA

This study is designed to develop Artificial Intelligence(AI)based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays(CXRs).The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of *** this study,AI-based analysis tools were developed that can precisely classify COVID-19 lung *** available datasets of COVID-19(N=1525),non-COVID-19 normal(N=1525),viral pneumonia(N=1342)and bacterial pneumonia(N=2521)from the Italian Society of Medical and Interventional Radiology(SIRM),Radiopaedia,The Cancer Imaging Archive(TCIA)and Kaggle repositories were taken.A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was ***,the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning(ML)algorithms,which twice trained the learning *** ResNet101 with optimized parameters yielded improved performance to default *** extracted features from ResNet101 are fed to the k-nearest neighbor(KNN)and support vector machine(SVM)yielded the highest 3-class classification performance of 99.86%and 99.46%,*** results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of *** proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

关键词： COVID-19 deep learning(DL) lung infection convolutional neural network(CNN)

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