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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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An Ingenious IoT Based Crop Prediction System Using ML and EL

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computers, Materials & Continua 2024年第4期79卷 183-199页

作者： Shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali Department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

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Comparative Analysis of Dynamic data Placement Strategy in Distributed Cloud Environment 1st

Comparative Analysis of Dynamic Data Placement Strategy in D...

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1st International Conference on data engineering and Machine Intelligence, ICDEMI 2023

作者： Shankar, B. Prabhu Singh, Uday Kumar Vijayalakshmi Viji, C. Rajkumar, N. Stalin, M. Nachiappan, Balusamy Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bangalore India Prologis DenverCO80202 United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

In general, the dynamic data optimization technique is used to place the user’s data in a cloud system to reduce the latency, intercommunication traffic, data transfer cost, execution cost, load balancing, and bandwidth utilization. The user data is dispersed and stored in a distributed cloud environment, so it must ensure data availability and reliability in cloud data centers. These data placement techniques involve two steps: first user data identification and second identifying the node to place the data in a geographically distributed cloud. The primary goal of the data placement approach is to find the best or optimal node to store user data in terms of latency and storage costs. This comparative analysis provides various survey data placement algorithms and techniques used to analyze latency delay, execution time, access cost, load balance, server over, bandwidth, and server capacity with different parameters. This study also analyzes the merits and demerits of data placement strategies. This survey helps to concentrate on the dynamic data optimization technique in the Distributed or multiple cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed cloud

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Natural Language Translation Engine For Announcements and Information Dissemination At Stations 2

Natural Language Translation Engine For Announcements and In...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Reedy, Medarametla Varshitha Abdul Naeem, B. Reddy, Tunga Kartikeya Benedict Vinusha, V. Pramila, R. Priyanka R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai601206 India

ISBN: (纸本)9798350328202

In today's fast-paced global travel landscape, effective communication is crucial, especially in dynamic environments like railway stations. This research paper addresses linguistic diversity challenges by developing a cutting-edge natural language translation engine tailored for railway communication. It integrates advanced technologies such as Transformer-based Automatic Speech Recognition (ASR) with noise reduction algorithms like Wav2Vec 2.0 and an Attention-based Convolutional Recurrent Neural Network (CRNN) to overcome background noise and diverse speech patterns. The system also includes multilingual Text-to-Speech (TTS) and Machine Translation (MT) capabilities for clear communication in real-time across multiple languages. Interactive features like a responsive Chatbot and Interactive Voice Response System (IVRS) enhance user engagement. This approach meets passengers' linguistic needs while promoting inclusivity. Technically, the project explores algorithms like the Transformer model, positional encoding, self-attention mechanisms, and feed-forward neural networks. The research aims to revolutionize railway communication and establish a precedent for technology-driven public transportation, creating universally accessible and user-friendly travel experiences. © 2024 IEEE.

关键词： Recurrent neural networks

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ENERGY-EFFICIENT ANT ROUTING ALGORITHM FOR OPTIMIZED PATH SELECTION NETWORK LONGEVITY IN WIRELESS SENSOR NETWORKS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第2期84卷 21-42页

作者： Zelani, Shaik Khader Babu, D. Nagabhushana Surendra, Doddaga Rahman, Mohammad Zia Ur Department of Electronics and Communication Engineering Sri Vasavi Institute of Engineering and Technology-Autonomous Pedana India Department of Data Science Malla Reddy University Telangana Hyderabad India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation K L Deemed to be University Vaddeswaram Andhra Pradesh India

The exponential growth of wireless sensor networks (WSNs) in various applications underscores the critical need for energy-efficient routing protocols to extend network lifespan. This paper introduces the Energy-Efficient Ant Routing Algorithm, a novel bioinspired routing strategy derived from ant colony optimization (ACO), which integrates traditional pheromone mechanisms with dynamic energy-aware adaptations to prioritize routes through nodes with higher residual energy levels, thereby balancing load and prolonging network functionality. The model’s adaptive pheromone updating mechanism, which considers both the cumulative path energy cost and the minimum residual energy of nodes along the path, encourages the use of energy-efficient routes and mitigates rapid energy depletion in critical network areas. This approach not only extends operational network lifetime but also supports higher data reliability and throughput in challenging WSN scenarios. The results of the proposed model demonstrated significant improvements over traditional ACO and other common energy-aware routing protocols. The network lifespan is extended by ~ 25% compared to standard ACO and by 15% compared to the best-performing conventional energy-aware routing protocol under the same conditions. Furthermore, EEARA achieved a 20% reduction in average energy consumption per node and enhanced the data delivery ratio by 10% due to more stable routing paths. The results underline the model’s potential as a scalable, efficient routing solution for energy-limited networks, promising substantial improvements in sustainable network operations. © 2025 by Begell House, Inc.

关键词： Ant colony optimization

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Enhanced automated web scraping tool with proliferation of AI techniques

Enhanced automated web scraping tool with proliferation of A...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Manikantam, Sadam Akhil, Patha Reddy, Komma Reddy Ashrith Prasada Reddy, G.S. Hariharan, Shanmugasundaram Kekreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319019

The proliferation of web technologies and artificial intelligence (AI) has paved the way for advanced web applications capable of intelligent behavior. This paper explores the integration of AI into web development to create a dynamic system that personalizes user experience and automates web interactions. We present a novel web-based AI framework developed using contemporary programming languages and libraries, detailed in the accompanying GitHub repository. Our methodology focuses on the use of machine learning algorithms to process and analyze user data in real-time, facilitating adaptive responses by the web application. The system's architecture is designed for scalability and efficiency, ensuring quick deployment and low latency in web interactions. Results from initial testing show promising improvements in user engagement and satisfaction. The discussion highlights the potential of this framework in revolutionizing user interaction across various web platforms, suggesting a significant step toward more intelligent and responsive web services. The paper concludes by suggesting future enhancements, including the integration of more robust AI models and expanding the framework's adaptability to different web environments. The research provides a foundational step for web developers and AI researchers interested in the convergence of these fields. © 2024 IEEE.

关键词： Web services

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Multitasking Agricultural Robot for Farmer Assistance using Internet of Things 14

Multitasking Agricultural Robot for Farmer Assistance using ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Saranya, S. Thilak, K. Deepa Kalaiselvi, K. Arthi, K. Kumaresan, K. Department of Electronics and Communication Engineering Easwari Engineering College India Department of Networking and communications SRM Institute of science and Technology Chennai India Department of Computer science and Engineering KSR College of Engineering India

Farming was the fundamental improvement in the climb of inert human development, by which developing of local species made food overabundances that enabled people to live in metropolitan networks. The verifiable setting of cultivating began centuries earlier. Plants were independently evolved in somewhere near 11 region of the world. Present day agribusiness considering tremendous degree monoculture in the twentieth century came to lead plant result, but around 2 billion people really depended upon asset cultivating. It is the chiefwellspring of food, grub and fuel. It is the fundamental preparation of money related new development. The foremost unbiased of this undertaking is to design a playing out numerous assignments robot that fill various jobs of Agriculture and helps in aiding farmersin the field. It is planned to restrict made by farmers despite accelerate and precision of thework and helps in higher creation and yield. The control and noticing rely upon web of things, because of the veritable of the field status can be checked from wherever in the world using web[1]. In our endeavor used in two control system i.e., field control and robotcontrol. In field control the soil sogginess sensors and ultrasound sensors resources and updates field data through PIC Microcontrollerand the field is obliged by sensible exercises ofthe information is passed on to the farmer by an application. The robot having a camera to give the field of the vision so while it plays out its fundamental assignments, we can screen everything. The whole system can be changedto mechanized mode for limitless oversight ofthe property by controlled the robot by farmer. © Grenze Scientific Society, 2023.

关键词： Cameras

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Optical Character Recognition Technology using Machine Learning

Optical Character Recognition Technology using Machine Learn...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kumar, B Anand Tamilarasan, S. Kiran, Ajmeera Tejaswi, Bagepally Sree, Manikonda Geethika Dasarla, Mallikarjun MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India HKBK College of Engineering Department of Information Science and Engineering Nagawara Bengaluru India

ISBN: (纸本)9798350348217

The creation of software that is capable of performing OCR in multiple languages is the goal of this project. The primary objective of optical character recognition (OCR) software is to convert pre-existing paper documents or picture files into digital versions that can be edited. Character and word detection are the two stages that are involved in the operation of multilingual OCR software. In the case of a method that is more complex, an OCR will additionally operate on the detection of sentences in order to maintain the structure of a document. Our project's goal was to better manage some of the more complicated recognition issues, such as distinct language alphabets, by better understanding, utilising, and improving the open source Optical Character Recognizer (OCR) software. This was our project's primary objective. © 2023 IEEE.

关键词： Optical character recognition

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Improved Image Caption Generation With GPT-3 And Deep Learning Approach 11

Improved Image Caption Generation With GPT-3 And Deep Learni...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Manchala, Laxman Teja Kallem, Sheshanth Reddy Kunja, Hemanth Soundararajan, Surendhar Hariharan, Shanmugasundaram Kukreja, Vinay Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

Subtitling, a growing field of natural language processing (NLP) research, includes generating a caption representation of images. The major goal is to use the pre-trained convolutional neural network (CNN) to develop a mechanism to caption an image. Convolutional neural network helps in feature extraction in this study whereas the extracted features are utilized by Recurrent Neural Network (RNN) to subtitle the captions for images. To encode the image several convolutional neural network pre-trained models are used. The decoder, a GPT -3-based language model, constructs descriptive sentences. In addition, adding Bahdanau's attention model to GPT -3 improves performance by enabling targeted learning in specific image regions. In an empirical evaluation of the MSCOCO dataset, our approach shows competitive performance. © 2024 IEEE.

关键词： Recurrent neural networks

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Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

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computers, Materials & Continua 2023年第5期75卷 3993-4008页

作者： Sarkar Hasan Ahmed Adel Al-Zebari Rizgar R.Zebari Subhi R.M.Zeebaree Computer Networks Department Sulaimani Polytechnic UniversitySulaimaniIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related *** has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic *** article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these *** presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart *** attain this,the presented ODLTCP-HRRSI model performs two major *** the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic ***,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)*** experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

关键词： Remote sensing images traffic prediction deep learning smart cities intelligent transportation systems

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