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Ambient intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Rahul Ghotekar Kailash Shaw Minakhi Rout Department of Computer Science and Engineering KIIT University Odisha India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Pune Maharashtra India

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional Neural Networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional machine learning and Deep learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets.

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Development of Secure Hardware Architectures for Preventing Side-Channel Attacks and Protecting Sensitive Data in VLSI Systems 4

Development of Secure Hardware Architectures for Preventing ...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kumara, Varuna Jayakumar, S. Rani, D. Leela Maranan, Ramya Ramesh, D. Raja Deivakani, M. Moodlakatte Institute of Technology Kundapura Department of Artificial Intelligence and Machine Learning Karnataka Kundapura576217 India Sri Sairam College of Engineering Anekal Department of Electronics and Communication Engineering Bengaluru562106 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India Chintalavalasa Department of Electronics and Communication Engineering Vizianagaram535005 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350300888

This research paper has been developed by targeting to develop the secure hardware architecture for preventing the potential threats like side channel attack and protecting sensitive data in VLSI systems. In this context, major of the key security frameworks like information theory, cryptography, online threats protector has been implemented in this study. It has been found, on the basis of extensive simulations, that the innovative design effectively protects against side-channel assaults and cuts the chance of mistakes by half, from 16 to 1. This concept has the potential to deliver considerable advantages for a variety of different sectors, including the Internet of Things, the military, and aerospace. In order to improve the design's overall performance and investigate the ways in which it could be applicable to other VLSI systems, more study could be carried out. © 2023 IEEE.

关键词： Computer software

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Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach

arXiv

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arXiv 2024年

作者： Liu, Huanyu Cai, Jianfeng Zhang, Tingjia Li, Hongsheng Wang, Siyuan Zhu, Guangming Shah, Syed Afaq Ali Bennamoun, Mohammed Zhang, Liang School of Artificial Intelligence Xidian University China School of Computer Science and Technology Xidian University China School of Science Centre for AI and Machine Learning ECU School of Physics Maths and Computing Computer Science and Software Engineering UWA

Flowcharts and mind maps, collectively referred to as flowmind, play an important role in our daily, and many companies have developed dedicated tools. Hand-drawn flowminds offer significant advantages for real-time and collaborative communication. However, there is an increasing demand to convert them into a digital format for further processing. Automated conversion methods are crucial in addressing the challenges associated with manual conversion, such as the cost of time and learning. Previous works have proposed diverse sketch recognition methods. However, these methods face significant limitations in practical situations. Firstly, most methods are designed for specific fields, making it challenging to extend their usage to other fields. Additionally, none of these methods address the critical step of digital conversion after recognition, which is essential to users. Moreover, existing datasets exhibit significant biases relative to actual data, hindering the methods’ generalization ability. Our paper proposes the Flowmind2digital method and hdFlowmind dataset to address the aforementioned challenges. Flowmind2digital is the first comprehensive recognition and conversion method for flowminds, utilizing a neural network architecture and keypoint detection technology to enhance overall recognition accuracy. Our hdFlowmind dataset consists of 1,776 hand-drawn and manually annotated flowminds, covering 22 scenarios and surpassing existing datasets in size. Our experiments showcase the effectiveness of our method, with an accuracy rate of 87.3% on the hdFlowmind dataset, surpassing the previous state-of-the-art work by 11.9%. Additionally, our dataset demonstrates effectiveness, with a 2.9% increase in accuracy after pre-training and fine-tuning on Handwritten-diagram-dataset. We also highlight the importance of simple graphics for sketch recognition, which can improve accuracy by 9.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

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Multimodal Sentiment Analysis using Kernel Based Support Vector machine 2

Multimodal Sentiment Analysis using Kernel Based Support Vec...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Khanna, Ashish Gupta, Deepak Madhumala, R.B. Nitte Meenakshi Institute of Technology Department Artificial Intelligence and Machine Learning Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India

ISBN: (纸本)9781665493963

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Sentiment analysis

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machine learning model for predicting pancreatic fistula after pancreatoduodenectomy

Machine learning model for predicting pancreatic fistula aft...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Hager Saleh Nora El-Rashidy Eman Mohamed Ahmad M Sultan Ayman El Nakeeb Shaker El-Sappagh Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Faculty of Computers and Information Damnhour University Damnhour Egypt Gastroenterology Surgical Center Faculty of Medicine Mansoura University Mansoura Egypt Faculty of Computer Science and Engineering Galala University Suez Egypt

Pancreaticoduodenectomy (PD) is a complex surgery used mainly to treat tumors and other pancreas disorders. PD is considered one of the most challenging surgeries because it may have several complications, including bleeding and infections in the surgical area, temporary or permanent diabetes, and pancreatic leakage (PL), which may lead to morbidity and mortality. In this study, we build an accurate and medically oriented machine learning model that predicts PL after PD based on patient markers collected only before the PD operation. The study is made using a real-world dataset for 397 Egyptian patients. The proposed machine learning pipeline starts with a data preprocessing step that handles the missing data values by the median values. In the next step, diverse interpretable classifiers, including logistic regression, random forest, decision tree, support vector machine, XGBoost, and AdaBoost, are utilized to predict the PL. Hyperparameter optimization is done using grid search with k-fold cross-validation. The results indicate that XGBoost achieves the highest marks, outperforming the state-of-the-art techniques in several evaluation metrics (i.e., accuracy= 91%, precision= 90.96%, recall= 91.0%, F1-score= 90.97%, and AUC=89.78%. The resulting model is accurate enough to be medically relevant for PL prediction in real healthcare settings.

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Inter-Vehicular Communication Using Split Ring Resonator

Inter-Vehicular Communication Using Split Ring Resonator

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Ashok S Palanivel S Prasanth P Prianka R R Prabhu. V Vijayakumar Peroumal Electronics and Communication Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India School of Electronics Engineering Vellore Institute of Technology Chennai India

For any wireless communication system, having an antenna is essential. However, it can be challenging to evaluate them, especially when developing new technology for intelligent transportation that requires communication between vehicles, with traffic efficiency and safety being crucial. To assess antenna performance, a novel technique that considers the multipath environment can be used in the early product development stages. By analyzing the radiation patterns of Split Ring Resonators (SRR) antennas mounted in various positions on the vehicle in a V2X environment, voltage readings can be obtained at the receiving antenna port to generate received power cumulative distribution functions (CDFs). Using these parameters, the antenna radiation patterns are assessed through a multipath simulation program, which presents the results as received power CDFs.

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Wearable Sensor Based Cloud Data Analytics Using Federated learning Integrated with Classification by Deep learning Technique 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Munnangi, Ashok Kumar Rajeyyagari, Sivaram Sekaran, Ramesh Jikkiriya, Nashreen Begum Ramachandran, Manikandan Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Karnataka Bangalore562112 India Department of Artificial Intelligence and Machine Learning Malla Reddy University Telangana Hyderabad500100 India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Wearable sensors and person to person communication stages assume a key part in giving another strategy to gather patient information for productive medical care observing. In any case, persistent patient observing utilizing wearable sensors produces a lot of medical care information. Deep learning (DL) is a potential method for big data analytics since it can be utilized to quickly classify as well as analyse this plethora of data. This research proposes novel method in wearable sensor based cloud data analysis by federated learning as well as classification using DL. Input is collected by cloud architecture based wearable sensor and processed for noise removal as well as normalization. Cloud data is secured based on federated learning and classified using U-Net convolutional auto encoder radial neural networks. Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for different wearable sensor dataset. Proposed method attained accuracy 98%, precision 85%, recall 75%, F-1 score 66%, RMSE 55% and NSE 55%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Classification (of information)

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Deep learning Method to Analyze the Bi-LSTM Model for Energy Consumption Forecasting in Smart Cities

Deep Learning Method to Analyze the Bi-LSTM Model for Energy...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Balasubramaniyan, S. Kumar, P.K. Vaigundamoorthi, M. Rahuman, A. Kaleel Solaimalai, Gautam Sathish, T. Vidhya, R.G. Mailam Engineering College Department of Electrical and Electronics Engineering Mailam India Sri Sairam Engineering College Department of Physical Education Chennai India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Psna College of Engineering and Technology Department of Electronics and Communication Engineering Dindigul India University of Austin Georgie Tech Department of Artificial Intelligence and Machine Learning 30332 United States Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India Hkbk College of Engineering Department of Ece India

ISBN: (纸本)9798350313987

Smart cities and IoT solutions are improving urban efficiency, resource optimization, and public safety by using modern technologies. Deep residual Bi-LSTM (Long Short-Term Memory) models can analyze and forecast complicated and time-varying data. This study examines how the deep residual Bi-LS TM model might improve smart city and IoT solutions. The model has broad use since it captures long-term interdependence and extracts meaningful representations from sequential data. Traffic prediction, energy consumption forecasting, environmental monitoring, predictive maintenance, public safety, and emergency response are discussed. The deep residual Bi-LSTM model provides realtime insights, accurate forecasts, and quick data processing to improve smart city systems and IoT solutions, making cities more sustainable, efficient, and secure. © 2023 IEEE.

关键词： Bidirectional Long Short-Term Memory (Bi-LS TM) Internet of Things (IoT) Optimization

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Forecasting of Photovoltaic Power with ARO based AI approach

Forecasting of Photovoltaic Power with ARO based AI approach

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Mounika Nallur B M Nalini Zabiha Khan S Nayana Prasad N Achyutha G Manjula Masters in Computer Information Systems Murray State University USA Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE BGS College of Engineering and Technology Bangalore India

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. Systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work.

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Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification

arXiv

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arXiv 2024年

作者： Liu, Shiqi Liu, Sannyuya Sha, Lele Zeng, Zijie Gašević, Dragan Liu, Zhi Engineering Research Center of Educational Big Data Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China Centre for Learning Analytics Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Various machine learning approaches have gained significant popularity for the automated classification of educational text to identify indicators of learning engagement – i.e. learning engagement classification (LEC). LEC can offer comprehensive insights into human learning processes, attracting significant interest from diverse research communities, including Natural Language Processing (NLP), learning Analytics, and Educational Data Mining. Recently, Large Language Models (LLMs), such as ChatGPT, which are considered promising technologies for artificial General intelligence (AGI), have demonstrated remarkable performance in various NLP tasks. However, their comprehensive evaluation and improvement approaches in LEC tasks have not been thoroughly investigated. In this study, we propose the Annotation Guidelines-based Knowledge Augmentation (AGKA) approach to improve LLMs. AGKA employs GPT 4.0 to retrieve label definition knowledge from annotation guidelines, and then applies the random under-sampler to select a few typical examples. Subsequently, we conduct a systematic evaluation benchmark of LEC, which includes six LEC datasets covering behavior classification (question and urgency level), emotion classification (binary and epistemic emotion), and cognition classification (opinion and cognitive presence). The study results demonstrate that AGKA can enhance non-fine-tuned LLMs, particularly GPT 4.0 and Llama 3 70B. GPT 4.0 with AGKA few-shot outperforms full-shot fine-tuned models such as BERT and RoBERTa on simple binary classification datasets. However, GPT 4.0 lags in multi-class tasks that require a deep understanding of complex semantic information. Notably, Llama 3 70B with AGKA is a promising combination based on open-source LLM, because its performance is on par with closed-source GPT 4.0 with AGKA. In addition, LLMs struggle to distinguish between labels with similar names in multi-class classification. Our results provide a valuable benchmark for evalu

关键词： Classification (of information)

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