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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation 5

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Faculty of Applied Science Department of Applied Statistics Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798331505998

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields. © 2024 IEEE.

关键词： Spatio-temporal data

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

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4th International Conference on Computing, Networking, Telecommunications and engineering sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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Development of Resistive-type IoT-Based Methane Gas Concentration Transmitter Using SnO2 Material

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Journal of The Institution of Engineers (India): Series D 2023年第2期104卷 515-529页

作者： Ghosh, Animesh Mondal, Bikas Mondal, Santu Hazra, Tanmoy Biswas, Arindam Department of Electrical Engineering Techno Polytechnic Durgapur West Bengal Panagarh India Department of Applied Electronics and Instrumentation Engineering Asansol Engineering College West Bengal Asansol India Department of Computer Science and Engineering NSHM Knowledge Campus West Bengal Durgapur India Department of Mining Engineering Kazi Nazrul University West Bengal Asansol India Centre of IoT and AI Integration with Education-Industry-Agriculture and School of Mines and Metallurgy Kazi Nazrul University West Bengal Asansol India

Many conventional sensors are used in industries since industrial methane sensing is critical. In the present case, a SnO2 layer is the sensing medium for methane that decreases its electrical resistance with increase in methane concentration. A Wheatstone bridge detects the decrease in resistance, thereby converting it to a suitable voltage. Since the environment where methane gas would be present is hazardous, electrical communication is impossible through a physical network. Instead, the authors have taken the help of the Internet of Things (IoT) for data communication purpose which has become much more feasible nowadays for communicating data in a hazardous environment. The mathematical relationship of the methane concentration with the transmitted current and voltage is found by theoretically analyzing the proposed methane sensor. The authors have tested the proposed system of methane concentration measurement experimentally. The paper presents the experimental results with reasonable accuracy, sensitivity, and linearity. [Figure not available: see fulltext.]. © 2022, The Institution of Engineers (India).

关键词： Internet of things

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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ChatGPT in IoT Systems: Arduino Case Studies 33

ChatGPT in IoT Systems: Arduino Case Studies

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Petrovic, N. Konicanin, S. Suljovic, S. University of Niš Faculty of Electronic Engineering Department of Computer Science Niš18000 Serbia The Academy of Applied Technical Studies Belgrade Belgrade11000 Serbia

ISBN: (纸本)9798350347760

Since the beginning of this year, the novel large language model (LLM) based ChatGPT conversational agent has been in spotlight, due to its comprehensiveness across many fields - from novel writing to playing board games. In this paper, it is explored how it can be leveraged within IoT systems, taking into account both the novel scenarios enabled relying on ChatGPT's power of question answering and software development as well. As example, two case studies related to Arduino platform are considered: 1) ChatGPT-based predictions on sensor data collected by Arduino 2) model-driven automated Arduino code generation. © 2023 IEEE.

关键词： Software design

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Efficient Video Summarization with Hydra Attentive Vision Transformer 20

Efficient Video Summarization with Hydra Attentive Vision Tr...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Ali, Muhammad Shafqat Azhar, Muhammad Masood, Saba Lee, Bumshik Iqbal, Tanzeela Amjad, Adeen Comsats University Islamabad Department of Computer Science Sahiwal Pakistan Hong Kong Shue Yan University Department of Applied Data Science Hong Kong Hong Kong Chosun University Department of Information and Communication Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350395785

A video summary's objective is to produce succinct and condensed synopses that accurately depict the content of the original video while sacrificing none of its vital features. Effective deep summarization models have arisen in the field of video summarizing, made feasible by the advancement of gated recursive unit (GRU) and long and short-term memory (LSTM) technologies. However, if the video is very long, GRU and LSTM models are unlikely to capture long-term dependencies as well as they could otherwise. In recent years, significant progress in the field of supervised video summarization has been accomplished through the use of techniques involving sequence-to-sequence learning. However, it is important to remember that traditional recurrent neural networks (RNNs) have some limitations when it comes to modeling long sequences and that using transformers to represent sequences necessitates a large number of input parameters, resulting in increased computational complexity. We present a new video summarizing methodology that addresses the aforementioned issues by utilizing a Hydra Attention-based Vision Transformer framework. The suggested method captures long-term dependencies and extracts significant characteristics from video sequences well. The proposed architecture improves the accuracy and reduces the processing time of video summaries by using the capabilities of Hydra Attention and transformer-based sequence modeling. Our solution outperforms state-of-the-art approaches in terms of performance and computing economy in empirical evaluations of the SumMe and TVSum datasets. © 2023 IEEE.

关键词： Computational complexity

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Harnessing Deep Learning for Activity Recognition in Seniors' Daily Routines with Wearable Sensors 47

Harnessing Deep Learning for Activity Recognition in Seniors...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Department of Applied Statistics Faculty of Applied Science Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Wearable sensors are increasingly popular for monitoring the daily activities of older individuals, sparking significant interest in human activity recognition (HAR). Accurate and timely identification of these activities is crucial for providing personalized care and timely interventions to maintain the well-being of elderly individuals. However, existing HAR methods often need support with complex activity patterns, limited labeled data, and the need for real-time performance. To address these challenges, this study proposes a novel deep-learning approach called ResBiGRU for precisely identifying daily activities in older adults using wearable sensors. The ResBiGRU architecture combines residual connections with BiGRU networks to capture short-term and long-term dependencies in sensor data effectively. We also propose a data augmentation strategy to improve generalization performance. Evaluation of the HAR70+ dataset, consisting of activities performed by 18 elderly individuals, demonstrates superior performance of our ResBiGRU model with an accuracy of 97.39% and an F1-score of 97.74%. Furthermore, our model supports real-time inference, making it suitable for practical applications. Implementing the ResBiGRU approach could significantly enhance monitoring and care for older adults by accurately identifying their daily activities. This research advances HAR methods tailored for elderly individuals, opening new avenues for personalized healthcare and assisted living technologies. © 2024 IEEE.

关键词： Wearable sensors

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Optimizing Spatial Pattern Analysis in Serial Remote Sensing Images through Empirical Mode Decomposition and Ant Colony Optimization

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Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 52-60页

作者： J Srinivasan S Uma Saleem Raja Abdul Samad Jayabrabu Ramakrishnan Department of Computer Applications Madanapalle Institute of Technology&Science(MITS)Madanapalle 517325Andhra PradeshIndia Department of Information Technology Panimalar Engineering CollegeChennai 600123India Information Technology Department College of Computing and Information SciencesUniversity of Technology and Applied Sciences-Shinas 324Sultanate of Oman Department of Information Technology and Security College of Computer Science and Information TechnologyJazan UniversityJazan 45142Kingdom of Saudi Arabia

Serial remote sensing images offer a valuable means of tracking the evolutionary changes and growth of a specific geographical area over *** the original images may provide limited insights,they harbor considerable potential for identifying clusters and *** aggregation of these serial remote sensing images(SRSI)becomes increasingly viable as distinct patterns emerge in diverse scenarios,such as suburbanization,the expansion of native flora,and agricultural *** a novel approach,we propose an innovative method for extracting sequential patterns by combining Ant Colony Optimization(ACD)and Empirical Mode Decomposition(EMD).This integration of the newly developed EMD and ACO techniques proves remarkably effective in identifying the most significant characteristic features within serial remote sensing images,guided by specific *** findings highlight a substantial improvement in the efficiency of sequential pattern mining through the application of this unique hybrid method,seamlessly integrating EMD and ACO for feature *** study exposes the potential of our innovative methodology,particularly in the realms of urbanization,native vegetation expansion,and agricultural activities.

关键词： spatial pattern analysis EMD ACO

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Toward Anomaly Detection in the Blockchain Using Numerical Association Rule Mining 21

Toward Anomaly Detection in the Blockchain Using Numerical A...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Kramberger, Renata Fister, Iztok Kamisalic, Aida Zagreb University of Applied Sciences Department of Information Technology and Computing Zagreb Croatia University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350343366

With the global increase in the popularity of cryptocurrencies, the need for anomaly detection and fraudulent behavior is reaching an all-Time high. In our paper, we propose a novel method of anomaly detection with the use of Numerical Association Rule Mining with Differential Evolution. The experiment was conducted by using the Dogecoin blockhain, and the dataset contained all of the transactions from one month. Our results contained 303 rules, with the best fitness function value of 0.8. © 2023 IEEE.

关键词： Association rules

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