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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Jain, Prakhar Bahirat, Nishtha Yashwantrao Shibi, C.Sherin Babitha Lincy, R. Jency Rubia, J. Arunarani, A.R. SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Tamil Nadu Chengalpattu Kattankulathur India Sri Eshwar College of Engineering Computer and Communication Engineering Department Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Avadi India

ISBN: (纸本)9798350372120

This paper introduces a groundbreaking real-time grammar error detection and correction system (named 'Error Ears') that adapts to linguistic interactions. At the core of the proposed approach lies the utilization of the potent 'T5' model, renowned for its capability to comprehend and produce human-like text. The proposed workflow meticulously covers essential stages including data pre-processing, model training, and fine-tuning. By fine-tuning the 'T5' model on a diverse dataset of sentences with numerous grammatical errors, the goal is to empower it to distinguish complex contextual nuances in speech. Preliminary results indicate that 'Error Ears' represents a substantial advancement over traditional correctional systems. This system excels at swiftly identifying and rectifying intricate spoken language errors, encompassing structural and contextual details. What sets it apart is its ability to provide real-time contextual suggestions, rendering it especially valuable during virtual meetings, presentations, and everyday conversations. 'Error Ears' is poised to redefine spoken language communication standards by enhancing clarity and precision. Its potential utility extends across a spectrum of contexts and linguistic environments, ensuring more efficient and accurate oral communication. In the digital age, this study opens up new avenues for a better comprehension and expression of language, resulting in more effective and transparent communication. © 2024 IEEE.

关键词： Data handling

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IoT Wearable Breast Temperature Assessment System 7

IoT Wearable Breast Temperature Assessment System

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ashreetha, B. Dankan Gowda, V. Anandaram, Harishchander Nithya, B.A. Gupta, Neeraj Verma, Basant Kumar Department of Electronics & Communication Engineering College Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India Department of Computer Science School of Applied Sciences Reva University Karnataka Bengaluru India Department of Computer Science and Engineering SGT University Haryana Gurugram India

ISBN: (纸本)9781665464086

Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations. © 2023 IEEE.

关键词： Internet of things

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An Effective Automated Framework for Oral Cancer Detection by Enhanced Convolutional Neural Networks 12

An Effective Automated Framework for Oral Cancer Detection b...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Santhiya, M. Sindhuja, M. Jegatha, R. Manikandan, J. Rajalakshmi Engineering College Thandalam Department of Computer Science and Engineering Chennai India Faculty of Engineering and Technology Srm Institute of Science and Technology Kattankulathur Department of Computational Intelligence Chennai India Sri Sai Ram Institute of Technology Department of Information Technology Chennai India St. Joseph's College of Engineering Omr Department of Information Technology Chennai India

ISBN: (纸本)9798350318210

Oral cancer, an aggressive form of cancer affecting the mouth and throat, has been on an alarming worldwide increase due to tobacco use and smoking. Oral cancer presents various challenges such as high incidence rates, delayed diagnosis, inadequate treatment plans and late prognoses and outcomes;early detection is crucial in improving prognosis outcomes and survival prospects. Machine learning techniques have gained prominence for improving cancer diagnosis while decreasing mortality and morbidity;in recent years they were used to detect oral cancer, providing highly accurate classification tasks yet limited capabilities in terms of feature extraction capabilities. To address these limitations, we are currently employing a deep-learning approach. To this end, we have devised an automated framework encompassing feature extraction, preprocessing and classification. Contrast-limited adaptive Histogram Equalization (CLACHE), used for effective pre-processing purposes to increase image contrast and resolution, is one form of pre-processing while Gray Level Cooccurrence Matrix extracts statistical texture-based features;finally, a Modified Convolutional Neural Net (MCNN) was employed for accurate classification of oral cancer cases. Utilizing Kaggle Repository dataset, we used it to assess the performance of our framework, while comparing it against other high-performance methods. Our proposed framework had an accuracy rating of 99.13% which surpasses current state-of-the art approaches. © 2023 IEEE.

关键词： Feature extraction

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Two Surface Multipactor with Non-Sinusoidal RF Fields and Space Charge Effects 24

Two Surface Multipactor with Non-Sinusoidal RF Fields and Sp...

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24th International Vacuum Electronics Conference, IVEC 2023

作者： Iqbal, Asif Wen, De-Qi Verboncoeur, John Zhang, Peng Michigan State University Deparment of Electrical and Computer Engineering East LansingMI48823 United States Michigan State University Department of Computational Mathematics Science and Engineering East LansingMI48823 United States

ISBN: (纸本)9781665473217

We investigate two-surface multipactor induced by non-sinusoidal Gaussian-type RF fields by employing 3D electromagnetic Particle-in-cell (PIC) simulations using computer Simulation Technology (CST) Particle Studio. We analyze the effects of the RF amplitude and the full width at half maximum (and/or half minimum) (FWHM) on multipactor susceptibility and the time dependent physics. We find that with Gaussian-type RF operation, the threshold peak RF voltage as well as the threshold time averaged RF power per unit area for multipactor development increases compared to that with sinusoidal RF operation. Shrinkage of multipactor susceptibility bands is observed in the presence of space-charge effects. © 2023 IEEE.

关键词： Electric space charge

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Hybrid CNN Classifier for Accurate Detection of Oral Cysts in Panoramic Dental Radiograph 9

Hybrid CNN Classifier for Accurate Detection of Oral Cysts i...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivagami, S. Senthilvadivu, S. Geetha, P. Yukthavarshika, J. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377972

In dentistry, machine learning approaches play a significant role in the early detection and diagnosis of oral cysts. When planning accurate medical treatment, oral and maxillofacial surgeons can benefit from preoperative detection of these cysts. Surgical planning is directly affected by classifying oral cysts as dentigerous, radicular, ameloblastoma, odontogenic carcinoma, and odontogenic keratocyte. In dentistry, the outcomes of differential diagnosis based solely on imaging are unacceptable. This research proposes a three-step framework for autonomous cyst classification, segmentation, and detection based on a Convolutional Neural Network (CNN). To begin, DENT-CNN, a modified CNN architecture is proposed to classify the oral cyst as normal or abnormal. Second, UNet is a CNN variant used to segment the categorized cyst regions on panoramic dental images. Third, the Gray Level Co-occurrence Matrix (GLCM) is combined with CNN to identify segmented cysts as benign or malignant. The proposed work is extensively evaluated using 1500 real-time dental panoramic radiographic images of the oral cyst collected from a dental hospital. To alleviate overfitting, the data augmentation approach is used. The performance of the proposed work is analyzed in terms of a cyst classification rate of 96.8 %, a Dice Score of 96.3 % for cyst segmentation, and an accuracy for the Identification phase of 96.5%. © 2024 IEEE.

关键词： Dentistry

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The Complexity of Approximate Pattern Matching on de Bruijn Graphs 26th

The Complexity of Approximate Pattern Matching on de Bruij...

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26th International Conference on Research in computational Molecular Biology, RECOMB 2022

作者： Gibney, Daniel Thankachan, Sharma V. Aluru, Srinivas School of Computational Science and Engineering Georgia Institute of Technology Atlanta United States Department of Computer Science University of Central Florida Orlando United States

ISBN: (纸本)9783031047480

Aligning a sequence to a walk in a labeled graph is a problem of fundamental importance to computational Biology. For finding a walk in an arbitrary graph with |E| edges that exactly matches a pattern of length m, a lower bound based on the Strong Exponential Time Hypothesis (SETH) implies an algorithm significantly faster than O(| E| m) time is unlikely [Equi et al., ICALP 2019]. However, for many special graphs, such as de Bruijn graphs, the problem can be solved in linear time [Bowe et al., WABI 2012]. For approximate matching, the picture is more complex. When edits (substitutions, insertions, and deletions) are only allowed to the pattern, or when the graph is acyclic, the problem is again solvable in O(| E| m) time. When edits are allowed to arbitrary cyclic graphs, the problem becomes NP-complete, even on binary alphabets [Jain et al., RECOMB 2019]. These results hold even when edits are restricted to only substitutions. Despite the popularity of de Bruijn graphs in computational Biology, the complexity of approximate pattern matching on de Bruijn graphs remained open. We investigate this problem and show that the properties that make de Bruijn graphs amenable to efficient exact pattern matching do not extend to approximate matching, even when restricted to the substitutions only case with alphabet size four. Specifically, we prove that determining the existence of a matching walk in a de Bruijn graph is NP-complete when substitutions are allowed to the graph. In addition, we demonstrate that an algorithm significantly faster than O(| E| m) is unlikely for de Bruijn graphs in the case where only substitutions are allowed to the pattern. This stands in contrast to pattern-to-text matching where exact matching is solvable in linear time, like on de Bruijn graphs, but approximate matching under substitutions is solvable in subquadratic O~(nm) time, where n is the text’s length [Abrahamson, SIAM J. Computing 1987]. © 2022, The Author(s), under exclusive license t

关键词： Graphic methods

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Investigation of Security Attacks in IoMT Devices and Federated Learning as a Mitigation Strategy

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Procedia computer science 2025年 258卷 3426-3435页

作者： Kiruthika M T Poongodi Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu 603203 India Professor Department of Computer Science and Engineering School of Engineering Dayananda Sagar University Bangalore Karnataka India

Internet of Medical Things (IoMT) is transforming the healthcare business with an increasing number of wearables, gadgets, and sensing devices. As a result, several patients are under daily monitoring for their cardio health, blood glucose level, blood pressure, etc. The data generated by these devices are very voluminous, and there is an increasing need for safeguarding these sensitive data in order to protect them from malicious attacks. This paper suggests to incorporate Federated learning model to IoMT as a mitigative measure. It is apt to induct FL into IoMT as it operates on a collaborative environment which do not allow transfer of the raw data. Patients’ data are stored at the client end itself and only model updates are shared to the server. In this way, the data and network attacks can be reduced to a great extent. This work proposes an integrated architecture where the layers of FL and IoMT can be knotted up for ensuring privacy and security. This proposed architecture model, further elaborates a distributed working environment. This paper illustrates how FL helps in securing the data produced by the different IoMT devices through the proposed architecture. The challenges involved in the deployment of FL in the framework of IoMT along with potential research directions are discussed further in this research.

关键词： : Federated Learning IoMT Security Internet of Medical Things IoMT Data Attacks Optimization

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Noise Robustness of Data Re-Uploading Quantum Classifiers 4

Noise Robustness of Data Re-Uploading Quantum Classifiers

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Molina, Daniel E. Hamilton, Kathleen University of Southern California Ming Hsieh Electrical and Computer Engineering Department Los AngelesCA United States Computational Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9798350343236

We investigate the robustness of quantum neural networks (QNNs) trained as binary classifiers against bit flip and depolarizing noise, two channels that emulate a subset of noise present in noisy intermediate-scale quantum (NISQ) devices. The presence of decoherence and other forms of noise poses significant challenges to classification accuracy. To assess the impact of noise on label extraction, and to evaluate the performance and robustness of our models, we trained three different label assignment methods in the presence of various levels of noise. Each QNN model uses 2 qubits to implement a parameterized model and either the qubits of the QNN, or a set of ancilla qubits are used for label extraction. These three different methods project the state of either a single qubit or multiple qubits onto the computational basis. This generates a probability distribution that is post-processed to assign a class label and a class probability. Each QNN is trained using noisy numerical simulation on a dataset with 2 features. Depolarizing noise channels are interleaved with the parameterized gates of the QNN and we include bit flip error prior to extracting the labels. The noise levels for the depolarizing noise and bit flip error are swept over large ranges of values. We deploy QNNs trained at various noise levels on IBM quantum computers- this introduces an additional source of noise due to SWAP gate noise. We observe that the QNNs trained with simulated noise can have good performance on NISQ hardware. © 2023 IEEE.

关键词： Depolarization

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Optimizing Forwarding Strategies in Sparsely Delay-Tolerant Networks with Short Contact Durations 15

Optimizing Forwarding Strategies in Sparsely Delay-Tolerant ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nemera, Yadesa Bedasa Jule, Leta Tesfaye Sivakumar, S. Pavithra, J. Sampathkumar, A. Rastogi, Ravi Dambi Dollo Univeristy Department of Information Technology Ethiopia Dambi Dollo University College of Natural and Computational Science Department of Physics Dambi Dollo Ethiopia Nehru Institute of Engineering and Technology Department of Cse Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Artificial intelligence and data science Coimbatore India Dambi Dollo Univeristy Department of Computer Science Ethiopia Koneru Lakshmaiah Education Foundation Department of Cse AP Guntur India

ISBN: (纸本)9798350370249

Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes is constrained by the mobility of nodes and the restricted bandwidth of peer- to-peer wireless transmission. In sparsely delay-tolerant networks, the re-establishment of contact between pairs of nodes is significantly delayed beyond the duration of the initial contact. Hence, the need for careful thought in designing a buffer control forwarding policy arises due to the limited contact period. Although forwarding algorithms have been designed extensively, much research has not been done on forwarding when there is a short contact time. When the message exchange between the pairs of network nodes is not completed during the contract period, they must wait until they are within communication range once more. To tackle the issue, we have suggested an innovative forwarding policy in buffer management that considers the duration of interaction. To assess the effectiveness of the proposed effort, we utilized the Opportunistic Network Environment (ONE) networking simulator. The suggested approach is combined with the MaxProp routing protocol buffer control forwarding policy and evaluated using two distinct metrics: bundle delivery rate and average delivery delay. In addition, we evaluated the performance of the suggested work by considering the number of nodes, buffer size, and simulation time. Across all the trials conducted in this thesis, the simulation results consistently demonstrate that our suggested method beats the existing forwarding strategy. © 2024 IEEE.

关键词： Buffer Management Forwarding Policy Contact Duration DTN Sparse DTN

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Decoding Financial Markets: Unleashing the Power of Bi-LSTM in Sentiment Analysis for Cutting-Edge Stock Price Prediction

Decoding Financial Markets: Unleashing the Power of Bi-LSTM ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Aluvala, Srinivas Balamurugan, V. Jeyasudha, J. Kumar, R. Dinesh Pradeep Kumar, S. Sr University School of Computer Science & Artificial Intelligence Warangal India Sathyabama Institute of Science and Technology Department of Ece Chennai India Srm Insitute of Science and Technology Department of Computational Intelligence Chennai India Saveetha School of Engineering Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Predicting stock prices accurately is a challenging task due to various influencing factors. This paper addresses the need for precise stock price prediction by presenting an enhanced sentiment analysis methodology using deep learning, particularly focusing on the Bi-LSTM model. The challenge of accurate sentiment analysis in stock prediction is overcome through advanced data preprocessing techniques tailored to maintain sentiment context effectively. The proposed Bi-LSTM (Bidirectional long Short-Term Memory) and CNN (Convolutional Neural Network) model demonstrates superior performance in classifying sentiments with high precision, recall, F1-score, and accuracy. The numerical validation demonstrates the effectiveness of the Bi-LSTM model in achieving a balance across various metrics, highlighting its potential for practical implementation in stock market prediction. The Bi-LSTM and CNN model achieves remarkable precision 90.00%, recall 90.00%, F1-score 90.00%, and accuracy 91.00%. In comparison to established methods, such as Multivariate Sequential Long Short-Term Memory Autoencoder (MSLSTMA), long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), the Bidirectional long Short-Term Memory (Bi-LSTM) model exhibits its competitive edge in stock price prediction as supported by evaluation metrics. The results emphasize the importance of tailored data preprocessing and deep learning techniques, showcasing the efficiency of the proposed methodology in enhancing stock price prediction accuracy while maintaining high percentages of precision, recall, and F1-score, along with impressive accuracy. © 2023 IEEE.

关键词： Bidirectional long Short-Term Memory Convolutional Neural Network Deep Learning Sentiment Analysis Stock Price Prediction

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