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RFID Thermal Monitoring Sheet (R-TMS) for Skin Temperature Measurements during Superficial Microwave Hyperthermia Treatment 7

RFID Thermal Monitoring Sheet (R-TMS) for Skin Temperature M...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Lestini, Francesco Panunzio, Nicoletta Marrocco, Gaetano Occhiuzzi, Cecilia University of Rome Tor Vergata Pervasive Electromagnetics Lab Civil Engineering and Computer Science Engineering Dept. Rome Italy

ISBN: (纸本)9789532901160

Hyperthermia is a widely used anti-cancer treatment that exploits the interactions between high-power electromagnetic fields and the human tissues to locally release a great amount of power in the underlying tissue regions. The electromagnetic source is generally outside the body, in close proximity to the skin. Thus, dangerous hotspots may occur within the skin, and hence continuous monitoring of the superficial temperature distribution is required throughout the treatment. Current Thermal Monitoring Sheets (TMSs), that are grids of many temperature sensors, have some limitations in terms of poor spatial resolution and high thermal conduction errors. Passive Ultra High Frequency (UHF) Radio Frequency IDentification (RFID) devices could offer an attractive alternative thanks to their wireless nature, low-invasiveness, and demonstrated good on-skin performance, especially for temperature monitoring. The preliminary design of an RFID-based Thermal Monitoring Sheet (R-TMS) is presented in this paper as a planar array of circular loop antennas with temperature-sensing-oriented RFID ICs. Both sensing and electromagnetic requirements of the R-TMS are addressed by means of numerical simulations. It proved capable of transmitting the collected temperature data with a remarkable spatial resolution, yet without interfering with the effectiveness of the hyperthermia treatment nor inducing unwanted hotspots over the skin. © 2022 University of Split, FESB.

关键词： Frequency selective surfaces

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Fake News Prediction in Hindi and English Robust Transformer Model

Fake News Prediction in Hindi and English Robust Transformer...

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

作者： Sanmugasundaram, R. Dileepan, D. Manikandan, M. Devi, M. Kiruthiga Ramya, N. Rajalakshmi Institute of Technology Dept. of Electronics and Communication Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Electronics and Communication Chennai India Dr. M.G.R. Educational and Research Institute Dept. of Information Technology Chennai India Sri Sairam Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

The ever-growing consumption of information from digital platforms such as social media has paved the way for information dissemination like never before, but not necessarily all the information or news delivered to us is authentic or holds true to reality. A huge amount of information is created and shared by social media users, some of which are manipulative and have no influence on reality, and can cause harm in a variety of ways. Implementation of an automated or AI-based system to classify text articles as misinformation is a challenging task as identifying the truth is complicated. An AI-based fake news prediction system is proposed to classify fake news from real ones by understanding the context of news and making decisions accordingly. To identify fake news by examining the context of the news, a Bi-directional Encoder Representations from Transformers (BERT) model, a pretrained model with a more potent feature extractor, has been implemented. BERT is well suited for tasks like predicting fake news because of its extensive contextualizing capabilities. The novelty of this paper is that the proposed system is trained, tested, and evaluated in 2 languages, English and Hindi to develop an overall reliable system to predict fake news in both languages. Two models of BERT namely DeBERTa and XLM-RoBERTa are chosen for the prediction of fake news. Further improvement in the model is achieved by using the additional news data to pre-train this model. Also, the model is fine-tuned to fit the requirements and to get the optimum result possible. The performance comparison of the chosen models is made and it is observed that the XLM-RoBERTa has improved accuracy over DeBERTa while testing for both languages. © 2023 IEEE.

关键词： API interface DeBERTa encoder fake news GPU XLM-RoBERTa

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Designing of Creator’s Information Hub using Machine Learning

Designing of Creator’s Information Hub using Machine Learni...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Diksha Gabhane Ujwalla Gawande Sanjana Gaikwad Swati Gajarlewar Rita Bhawalkar Dept. of Computer Science & Design Yeshwantrao Chavan College of Engineering Nagpur India Dept. of Information Technology Yeshwantrao Chavan College of Engineering Nagpur India NCPharmacy Nagpur India Dept. of Cyber Security and Data Science G H Raisoni College of Engineering and Management Pune India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide.

关键词： Industries Technological innovation Video on demand Machine learning algorithms Education Ecosystems Collaboration Medical services Machine learning Web sites

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OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework

OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sen...

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International Conference on Distributed Computing Systems Workshop

作者： Cheng-Wei Ching Chirag Gupta Zi Huang Liting Hu Dept. of Computer Science and Engineering University of California Santa Cruz USA Dept. of Computer Science Virginia Tech USA

Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.

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Leveraging compiler optimization for code clone detection 33

Leveraging compiler optimization for code clone detection

引用

33rd International Conference on Software engineering and Knowledge engineering, SEKE 2021

作者： Singh, Shirish Singhal, Harshit Mishra, Bharavi Dept. of Computer Science Columbia University United States Dept. of Computer Science and Engineering The LNMIIT India

ISBN: (纸本)1891706527

Finding similar code in software systems can guide several software engineering tasks such as code maintenance, program understanding, and code reuse. Similar code detection has been actively studied in the past. In the paper, we propose a novel approach that leverages compiler optimizations to transform semantically similar code and detect similar programs. The key observation of our work is that the compiler optimizations can be used to smooth out source code level idiosyncrasies introduced by the developers, thus making the optimized programs, for the same task, similar in structure. The similarity in structure can then be used to classify the programs. We conducted experiments on the Google CodeJam dataset to demonstrate the effectiveness of our approach. The experimental results show that our technique can achieve up to 85% accuracy on the program classification task, which is an improvement of more than 25% over the source code level classification. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： computer software reusability

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Frameworks, Applications and Challenges in Streaming Big data Analytics: A Review 3

Frameworks, Applications and Challenges in Streaming Big Dat...

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Nabeel Mustafa, Syed Muhammd Umer Farooque, Muhammad Tahir, Muhammad Khan, Shariq Mahmood Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Sir Syed University of Engineering and Karachi Dept. of Software Engineering Pakistan

ISBN: (纸本)9781665461412

data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data"refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-Time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-Time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big data analytics are applied as well as the difficulties encountered. © 2022 IEEE.

关键词： Big data

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Enhancing Intrusion Detection Systems for Secure E-Commerce Communication Networks

Enhancing Intrusion Detection Systems for Secure E-Commerce ...

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2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023

作者： Vullam, Nagagopiraju Roja, D. Rao, NagaMalleswara Vellela, Sai Srinivas Vuyyuru, Lakshma Reddy Kumar, K Kiran Chalapathi Institute of Engineering and Technology Dept. of Cse AP Guntur India Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of Cse AP Guntur India Koneru Lakshmaiah Education Foundation Dept. of Cse AP Vaddeswaram India

ISBN: (纸本)9798350306088

Rapid advancements in information and communication technologies have permeated various sectors, leading to an exponential growth in Internet users. However, this surge in connectivity has also given rise to a corresponding increase in cyber-attacks. E-commerce applications, which have become integral to our daily lives, encompass online banking, marketing, trading, and numerous other online businesses. To safeguard these critical systems and networks, Network Intrusion Detection Systems (NIDS) play a pivotal role by thwarting unauthorized access and countering various cyber threats. Presently, the prevailing NIDS systems predominantly rely on the Backward Oracle Matching (BOM) algorithm. While effective in reducing false alarms, this algorithm has been associated with a high packet drop ratio. In light of these limitations, this paper examines the existing NIDS systems and the various pattern-matching techniques they employ, shedding light on their weaknesses and constraints. To overcome these shortcomings, the paper introduces an enhanced iteration of the BOM algorithm. This enhanced version leverages multiple pattern-matching methods, enhancing the overall performance of the NIDS system and subsequently fortifying network security. To validate the proposed solution, comprehensive simulations were conducted using established datasets such as Snort and NSL-KDD. The experimental results unequivocally demonstrate the superior performance of the proposed solution compared to existing approaches. Specifically, it achieved a remarkable 5.17% improvement in detection rate while concurrently reducing the false alarm rate by 0.22% compared to the existing solution. IN summary, this paper addresses critical issues inherent in current NIDS systems, providing a robust solution through an enhanced BOM algorithm that incorporates multiple pattern- Matching methods. The experimental findings underscore the effectiveness of this approach in bolstering network security, offering p

关键词： Network security

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Prioritized Semi-supervised Deep Embedded Clustering

Prioritized Semi-supervised Deep Embedded Clustering

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Saladi, Pranita Guntupalli, Rishi Manudeep Puppala, Sudheer Kumar Pulabaigari, Viswanath Indian Institute of Information Technology Sri City Dept. Computer Science Engineering Sri City India

ISBN: (纸本)9781665406703

Clustering, to group similar objects, is an important problem. Recently deep learning-based methods like Deep Embedded Clustering (DEC) [6] and its semi-supervised version called Semi-supervised Deep Embedded Clustering (SDEC) [12], where partially labeled data or data with constraints is available, are shown to give promising results. Both DEC and SDEC learn a latent space where similar objects are closer and dissimilar are away. While promising results are shown, the information present in constraints or a labeled subset of the data is not fully utilized. This paper proposes to use priorities for constraints so that important constraints are given more weightage than unimportant ones. Those constraints with points that are far away, but should be clustered into a group, gets more weight than other labeled points. Similarly, those in different groups which are very close get more weightage. The appropriate loss function is used in the learning process. The proposed method is called Prioritized Semi-supervised Deep Embedded Clustering (PSDEC). The results are compared using a few standard data sets against recent and classical similar methods. PSDEC is found to achieve a better result than un-prioritized constraints. © 2022 IEEE.

关键词： Interpolation

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Acoustic Scene Classification using Fusion of Features and Random Forest Classifier 9

Acoustic Scene Classification using Fusion of Features and R...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sachdeva, Shantanu Mulimani, Manjunath Manipal Institute of Technology Manipal Academy of Higher Education Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9789380544441

This paper proposes a model for the task of Acoustic Scene Classification. The proposed model utilizes convolutional neural networks and a random forest classifier to predict the class of the audio clips. The features used by the proposed model are log-Mel, Mel-frequency cepstral coefficient, and Gammatone cepstral coefficient spectrograms. Each spectrogram is processed using a convolutional neural network and combined into a single vector. The processed feature vectors are classified into one of the acoustic scenes using the random forest classifier. The proposed model is evaluated on Tampere University of Technology Urban Acoustic Scenes 2018 and the Tampere University Urban Acoustic Scenes 2019 development datasets. The performance of the proposed model is compared with the Detection and Classification of Acoustic Scenes and Events 2018 and 2019 challenge baseline models to show its efficacy. The proposed model has an accuracy of 68.1% and 67.1% for the two datasets, respectively. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Convolutional neural networks

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Preparing and Annotating a Set of Relevant Contextual Words from the Text Corpus and the Online Newspaper Archives in Bengali

Preparing and Annotating a Set of Relevant Contextual Words ...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Alok Ranjan Pal Prithwish Adhikari Abhishek Dutta Antara Pal Diganta Saha Dept. of Computer Science and Engg. College of Engg. and Mgmt. Kolaghat India Dept. of Computer Science and Engg. Atharva College of Engineering Mumbai India Dept. of Computer Science and Engg. Jadavpur University Kolkata India

Context analysis is one of the obvious tasks in nearly all computational linguistics based works. All of these works pursue this task upto different extents using different techniques. Most of the techniques include the linguistic features, like - N-grams, content words, function words, noun phrase and verb phrase, theme extraction with relevance scoring, facets etc. There are lots of machine learning and deep learning based tools available for this task. The performances of these tools are variable according to different situations. But, the close observations cite that the performance of these systems mainly depends on the availability of the contextual words at that particular experiment. Except for a good collection of contextual words, no system can produce an effective result by using only some complex mathematical calculations. This scenario is the motivation behind this work. In this work, an attempt has been made to construct a structured set of most relevant contextual words according to the classification of text domains, mentioned in the Technology Development for Indian Languages (TDIL) corpus. As it is a group work, therefore different categories of people have been involved in this work for preparing, annotating, verifying and validating the data sets.

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