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An oceanographic data collection scheme using hybrid optimization for leakage detection during oil mining in mobility assisted UWSN

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Multimedia Tools and Applications 2024年第42期83卷 89723-89741页

作者： Choudhary, Monika Goyal, Nitin Gupta, Deepali Sharma, Bhanu Sharma, Nonita Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Seth Jai Parkash Mukandlal Institute of Engineering and Technology Haryana Radaur India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India Department of Information Technology Indira Gandhi Delhi Technical University for Women Delhi India Immersive and Interactive Technology Lab Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data acquisition is the process of collecting, measuring and analysing information using standardised, validated techniques for application-specific tasks. In mobility-assisted underwater wireless sensor networks (UWSNs), where nodes are not fixed due to water current of 3 m/sec, data collection becomes an arduous task. There are few works that provide a mobile sink with optimised data transmission path planning and scheduling. These systems do not transmit the data fast enough to provide real-time data transmission as these methods do not consider the buffer occupancy rate and latency in data acquisition. In this paper, a stimulating transmission path planning technique for mobile sinks using the hybrid Grey Wolf Optimizer Whale Optimization Algorithm (GWOWOA) is proposed. In contrast to other optimization techniques, this hybrid technique includes a number of update processes such as random position update, prey search by the Grey Wolf Optimizer (GWO) and prey search by the Whale Optimization Algorithm (WOA). In this paper, the fitness function is calculated in terms of distance to the mobile sink, buffer occupancy rate, energy level and data acquisition latency. The use of these variables makes the proposed technique innovative. To prove the efficiency of the proposed system, GWOWOA is compared with existing systems. The simulation results show that the proposed system increases the residual energy and accuracy of the collected data and minimises the delay. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data acquisition

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Bilingual SMS Spam Detection Using Deep Ensemble Learning

Bilingual SMS Spam Detection Using Deep Ensemble Learning

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sultana, Hafsa Tanvin, Jamal Uddin Tasnia, Fairooz Sharmin, Nusrat Military Institute of Science and Technology Department of Computer Science and Engineering Mirpur Cantonment Dhaka1216 Bangladesh

ISBN: (纸本)9798331535476

Spam messages have become a pervasive issue in digital communication, posing significant challenges to users and organizations alike. As the volume of text-based interactions continues to grow, particularly in low-resource languages, e.g. Bangla-English SMS messages, the need for effective spam detection systems has never been more critical. This paper introduces BESpam, a novel bilingual dataset comprising 5,730 instances of Bangla-English SMS messages aimed at addressing this gap. We explore the development of an ensemble model that leverages advanced transformer architectures alongside FastText to enhance spam classification performance. Our findings indicate that this proposed ensemble approach outperforms traditional machine learning methods and standalone transformer models, demonstrating the efficacy of combining contemporary natural language processing techniques with established feature extraction methods. The ensemble stacking method achieved a 99% accuracy, surpassing both the individual performances of mBERT (98%) and XLM-RoBERTa (98%), as well as traditional models like Logistic Regression and SVM, both of which scored 98%. This research not only advances the field of s pam detection but also serves as a vital resource for future studies focused on multilingual text processing, particularly in regions where SMS spam remains a pressing concern. © 2024 IEEE.

关键词： Bilingual Ensemble FastText mBERT Meta-model SMS Spam Stacking XLM-RoBERTa

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From Data to Dialogue: Building a Domain-Agnostic Conversational Question Answering System

From Data to Dialogue: Building a Domain-Agnostic Conversati...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Patil, Pranav Mathews, Justin James, Joel Patil, Aditya Rukhande, Smita Fr. C. Rodrigues Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350382693

In the realm of natural language processing (NLP), the quest for domain-specific question answering has been a focal point, emphasizing the precise extraction of factual information from designated knowledge sources in response to targeted inquiries. This project introduces a versatile, adaptable open-domain question answering system primed for seamless integration across diverse sectors, including educational institutions, e-commerce platforms, and healthcare portals. Leveraging the prowess of BERT and TAPAS, augmented with RAG (Retrieval-Augmented Generation), our system offers a holistic solution catering to varied information retrieval needs. Through fine-tuning on domain-specific datasets, both BERT and TAPAS capitalize on their respective strengths: BERT's contextual understanding and TAPAS's structured reasoning capabilities. The ensemble model dynamically selects the most suitable approach for each query, ensuring flexibility in handling diverse linguistic nuances and query types. Moreover, the system integrates language translation and voice output features to further enhance its adaptability. Evaluation on benchmark datasets reaffirms the system's efficacy in delivering precise and coherent responses, underscoring its potential as a multifaceted tool for enhancing user interaction and information retrieval across educational, ecommerce, healthcare, and other domains. © 2024 IEEE.

关键词： Question answering

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Facial Recognition-Based Automatic Attendance Management System Using Deep Learning 2nd

Facial Recognition-Based Automatic Attendance Management Sys...

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2nd International Conference on Big Data, Machine Learning, and Applications, BigDML 2021

作者： Nath, Saranga Pani Borah, Manditjyoti Das, Debojit Kalita, Nilam Kumar Hussain, Zakir Borah, Malaya Dutta Department of Computer Science and Engineering National Institute of Technology Silchar Cachar Assam Silchar788010 India

ISBN: (纸本)9789819934805

Nowadays, computer-based face recognition, along with other biometric methods, is a mature and reliable mechanism that is widely used in many access control scenarios. Face recognition is divided into two parts: Face Verification and Face Identification. Face Verification evaluates whether two photographs are connected to one person or not by comparing them, whereas Face Identification must identify a specific face among a group of available faces in the database. Angle, illumination, position, facial expression, noise, resolution, occlusion, and the small amount of one-class samples with which to work are all obstacles in face identification. In this study, we use transfer learning in a siamese network, which comprises two comparable CNNs, to do facial recognition. A pair of two face photographs is supplied to the network as input, after which the network extracts the features of the pair of images, and eventually, using a similarity criterion, it assesses if the pair of images belongs to one person or not. The results suggest that the proposed model can compete with advanced models trained on datasets with a large number of samples. Furthermore, it enhances facial recognition accuracy when compared to algorithms that are trained using datasets with a small number of samples. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Alzheimer’s Disease Detection Using Convolution Neural Networks 1

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1st International Conference on Intelligent Computing for Sustainable Development, ICICSD 2023

作者： Swapna, M. Ravali, M. Pavani, G. Prasad, M Shiva Durga Kumar, V Pradeep Nanda, Ashok Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Medak India

ISBN: (数字)9783031612985

ISBN: (纸本)9783031612978

Millions of people throughout the world are afflicted by the progressive neurological condition known as Alzheimer’s Disease (AD). Analyzing MRI brain pictures is one of the most promising methods for identifying AD. Deep learning methods have become effective tools for diagnosing AD from MRI scans in recent years. An overview of deep learning-based methods for AD identification using MRI scans is given in this research, along with an explanation of the various deep learning architectures, including convolutional neural networks (CNNs) and auto-encoders. We explore the difficulties in detecting AD using deep learning, such as the necessity for big and diverse datasets and the possibility of bias and overfitting. MRI images have been used in recent studies to identify AD using deep learning, including the utilization of adversarial training and transfer learning. The proposed work will give a good accuracy where training accuracy is 86.34% and validation accuracy is 86.45% on the test data with very small misclassifications on normal and very mild demented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diagnosis

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Identifying The Established Semantic Kinds In Text Using Named Entity Recognition

Identifying The Established Semantic Kinds In Text Using Nam...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Seershika, Mitnasala Aishwarya, Vasuri Vani, V. Karthik, N. National Institute of Technology Puducherry Department of Computer Science of Engineering Puducherry Karaikal609 609 India

ISBN: (纸本)9798331540685

Named entity recognition (NER) is a natural language processing (NLP) method that extracts information from text. NER includes identifying and ordering significant data in text known as named elements. By approaching span-based method (where tokens are represented as spans) we can detect named entity for each span. However some current algorithms check every span separately without knowing previous span, which leads to ambiguity. To overcome this, a bi-directional two-dimensional operation is used, which uses knowledge of previous spans and compares the two spans and generates meaningful named entities. For implementation of the NER we have used the three public datasets. The experimental results in this paper shows that the method we used is very effective to generate meaningful nested named entities. © 2024 IEEE.

关键词： Named Entity Recognition Planarized sentence Self-Encoding

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Building Radio Dynamic Zones with OPENZMS

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1410-1426页

作者： Johnson, David Maas, Dustin Tadik, Serhat Orange, Alex Stoller, Leigh Webb, Kirk Awan, M. Basit Iqbal Bills, Jacob Gomez, Miguel Sarbhai, Aarushi Durgin, Greg Kasera, Sneha Patwari, Neal Schurig, David der Merwe, Jacobus Van University of Utah Kahlert School of Computing Salt Lake CityUT84112 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States University of Utah Department of Electrical and Computer Engineering Salt Lake CityUT84112 United States

Radio Dynamic Zones (RDZs) are being explored by the research community as an approach to safely test and evaluate spectrum sharing mechanisms and technologies. There is general consensus in the research community regarding the conceptual architecture of an RDZ. In this paper, we present our work on the, a prototype RDZ developed and built on the platform. We present a practical RDZ architecture and explore a number of end-to-end use-cases. We present the design and implementation of, our prototype RDZ Zone Management System, and evaluate it in the platform. © 2015 IEEE.

关键词： Monitoring computer architecture Radio frequency Powders Prototypes Jacobian matrices Interference Sensors Licenses Architecture

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Scalable Modular Battery Management System Using Model-Based Approach for EV Applications 2

Scalable Modular Battery Management System Using Model-Based...

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

作者： Kumar, Srinath Jawaharram, Vishnu Ram Balashivakumaran, R. Chandrakala, K.R.M. Vijaya Kumar, S. Sampath Nadar, Kannan Paulish Amrita School of Engineering Department of Electrical and Electronics Engineering Coimbatore India Institute of Technology Jijiga University Department of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798350328202

The global automotive industry is in the phase where Internal Combustion vehicles are in decline and witnessing a shift towards sustainable development. The major parameter of a successful EV is an efficient battery pack and the protection for it which is the Battery management system (BMS). In this work model-based approach in the design of a Modular Battery Management System (BMS) takes into account various protection schemes such as over and under-voltage scenarios adopted in Li-ion batteries and monitoring the State of Charge (SoC) using the Coulomb Counting technique. The design and the simulation-based approach are adopted to study the behaviour of Li-ion Battery and BMS in a wide operational condition where the testing is difficult using MATLAB/Simulink. The proposed technique is a Modular Battery Management System that works on the Master-Slave Architecture. © 2024 IEEE.

关键词： Battery management systems

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