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Joint 9th International Conference on Digital Arts, Media and technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

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

ISBN: (纸本)9798350318241

The use of widespread sensors for automatic activity recognition has become a dynamic area of research due to its broad applications in healthcare, fitness monitoring, and assisted living. To efficiently identify daily and sports activities from sensor data, this study suggests a hybrid deep learning model that integrates convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM) networks, and convolutional block attention modules (CBAM). The model starts by extracting spatial features from the sensor data using CNN layers. These spatial features are then employed to offer temporal context information to the BiLSTM network. To conclude, the CBAM attention mechanism is applied to guide the model's focus to the most informative segments of the BiLSTM feature maps. This approach enables the accurate recognition of patterns in both complex and daily sports activities. The effectiveness of the proposed hybrid model is evaluated using the UCI-DSA, a publicly available benchmark dataset that includes sports and daily activities collected through wearable motion sensors. In contrast to existing approaches, the outcomes reveal that a unified framework involving CNN, BiLSTM, and CBAM achieves outstanding performance in activity recognition, exceeding 99 % in both accuracy and F1-score while maintaining computational efficiency. © 2024 IEEE.

关键词： Wearable sensors

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Air Quality Image Classification using Ensemble Based Transfer Learning Techniques

Air Quality Image Classification using Ensemble Based Transf...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Saradha, S. Kumar, M. Praveen Asha, J. Saranya, R. Santhoshkumar, S.P. Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India PSG Institute of Technology and Applied Research Neelambur Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9781839539848

In recent years, research has focused on the efficacy of neural networks, Learning by Transfer (TL), and Ensemble Learning (EL) techniques in image processing. Ensemble approaches, when employed on image classification tasks improve the overall predictive performance and robustness. This work examines the studies related to ensemble approach and applies the same for the classification of images based on different air pollution levels. The proposed research work uses three Convolution Neural Networks (CNN) models namely VGG, MobileNet, and EfficientNet and ensembles them using stacking technique for air quality image classification. The results exhibited in the suggested model generated an overall classification accuracy of 95%. © The Institution of Engineering & technology 2023.

关键词： Image classification

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Comparative Analysis of Outlier Detection Algorithms in Agricultural IoT Datasets

Comparative Analysis of Outlier Detection Algorithms in Agri...

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Innovative Research in applied science, Engineering and technology (IRASET),Conference on

作者： Ahmed Amamou Bilal Ben Mahria Younes Balboul Said Hraoui Computer Science Department National School of Applied Sciences Fes Morocco Computer Science Department Faculty of Science and Technology Fes Morocco Network and Telecommunications Department National School of Applied Sciences Fes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This paper evaluates the efficiency of eleven outlier detection algorithms applied to three agricultural IoT datasets: Weather in Szeged, Greenhouse, and Crop Recommendation. It focuses on how data quality affects IoT systems' reliability in agriculture. The analyzed algorithms include statistical, distance-based, density-based, ensemble, and hybrid methods, and their ability to detect outliers is investigated using recall, precision, and F1-score. The results demonstrate that algorithm performance is influenced by dataset characteristics such as dimensionality, nonlinearity, and temporal dependencies. Isolation Forest (iForest) consistently outperformed other algorithms due to its non-parametric design and recursive partitioning. In contrast, algorithms like Cluster-Based Local Outlier Factor (CBLOF), K-Nearest Neighbors (KNN), and Gaussian Mixture Model (GMM) showed variable effectiveness depending on dataset features. The study emphasizes the need for selecting the appropriate outlier detection method tailored to specific agricultural datasets and recommends future research on hybrid models or deep learning approaches to enhance anomaly detection in agricultural IoT systems.

关键词： Deep learning Data integrity Clustering algorithms Forestry Nearest neighbor methods Partitioning algorithms Internet of Things Anomaly detection Gaussian mixture model Meteorology

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MANET’s Dependable Data Delivery Systems Through the Use of Global Timing Protocols

MANET’s Dependable Data Delivery Systems Through the Use of...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Chandravanshi, Kamlesh Soni, Gaurav Mishra, Durgesh Kumar School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh Indore India

ISBN: (纸本)9789819709748

One of the most important aspects of any network, whether wired or wireless, is keeping the transmitter and receiver’s time in sync. In a mobile ad hoc network, each node is constantly adapting to its immediate surroundings. Without oversight or centralized communication, the data sending and receiving is executed autonomously. A novel method for ensuring consistent communication in dynamic networks using clock synchronization and multicast routing is presented in this study. Group communication is possible using the multicast routing protocol;each group consists of many nodes, each of which has its own unique clock time and a designated coordinator. According to the average time beacon of all groups in MANET, the clocks of all mobile nodes are synchronized in the proposed study. One way to boost network efficiency is using the suggested multicast Average Time Clock beacon Synchronization (Avg-SYN-CLK) protocol. Compared to Asynchronies Clock Time Performance (ASYN-CLK) and the Maximum Clock Time Synchronization (MAX-SYN-CLK) beacon broadcasting method, the suggested global clock synchronization scheme outperforms both in terms of end-to-end delay and packet loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Synchronization

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A Scalable Design Approach for State Propagation in Serverless Workflow 3

A Scalable Design Approach for State Propagation in Serverle...

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3rd IEEE Global Conference for Advancement in technology, GCAT 2022

作者： Bharti, Urmil Goel, Anita Gupta, S.C. Shaheed Rajguru College of Applied Sciences for Women University of Delhi Department of Computer Science Delhi India Dyal Singh College University of Delhi Department of Computer Science Delhi India Indian Institute of Technology Department of Computer Science Delhi India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Serverless development is challenging as applications are composed of stateless and short-lived functions. Many workflows require time-bound functions to transfer their state to other function before termination. The serverless Function-as-a-Service offerings lack state management support;therefore, it must be handled at application-level. In this paper, we propose a scalable design approach that simplifies development of workflows that require sharing of ephemeral intermediate data. Our design uses object serialization/deserialization with cloud object storage to share state across functions. It provides a mechanism for fine-grained support for state propagation and synchronization in a serverless workflow. This solution is cost-effective and efficient as it does not depend on any external database or cache for state management. The design has been validated by implementing 'Word Count'- a classic MapReduce use case. Our results show that the proposed scalable design can process input of any size and can handle state propagation in complex serverless workflow. © 2022 IEEE.

关键词： Digital storage

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Inverse Design of Silicon Nitride Wavelength-division Multiplexers and Frequency-selective Resonators

Inverse Design of Silicon Nitride Wavelength-division Multip...

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2024 Frontiers in Optics, FiO 2024

作者： Liu, Danxian Bostan, Egemen Zeng, Tianyi Paul, Aditya Yang, Kiyoul John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

We experimentally demonstrate inverse-designed 980/1530 nm and 1480/1530 nm wavelength-division-multiplexers on an 800 nm-thick silicon nitride platform. The devices exhibit © Optica Publishing Group 2024, ©... 详细信息

关键词： Silicon nitride

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Exploring advanced feature selection techniques: an application to dialectal Arabic data

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International Journal of Information technology (Singapore) 2024年第7期16卷 4637-4649页

作者： Abdellah, Ait Elouli Ouahi, Hassan Cherrat, El Mehdi Bekkar, Abdellatif Department of Computer Science Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibn Zohr University Ait Melloul Morocco Laboratory of Innovation in Mathematics and Intelligent SystemsFaculty of Applied Sciences Ibn ZohrUniversity Ait Melloul Morocco Laboratory of Systems Engineering and Information Technology National School of Applied Sciences Ibn Zohr University Agadir Morocco Department of Computer ScienceLIM LaboratoryFaculty of Sciences and Technics Hassan II University of Casablanca Mohammedia Casablanca Morocco

In the field of automated language processing, distinguishing between Moroccan Arabic (Darija) in multilingual contexts is a major challenge. This study addresses this challenge by exploiting feature selection techniques to improve the accuracy of tongue detection. Using a comprehensive methodology integrating various feature selection methods, including TF-idf, CBOW, Word2Vec for feature extraction, LASSO regression, decision trees for machine learning techniques, and statistical feature selection such as ANOVA, Pearson correlation coefficient, and Mutual information, alongside semantic techniques by developing our semantic encoders adapted to Arabic data, we strive to uncover the most important, relevant language features while attenuating the noise. As a result, we have obtained robust results by applying the famous XGBOOST, with conventional extraction methods and SVD as a dimensional reduction method with a very reasonable execution time. Our experimental results highlight the effectiveness of feature selection techniques to reinforce supervised learning tasks. This research not only advances the field of natural language processing but also highlights the central role of feature selection in deciphering complex linguistic landscapes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Comparative Analysis Dialect Identification Dimensionality Reduction Moroccan Dialect Arabic NLP Standard Arabic

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Machine Learning and its Application in Food Safety 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Rahul, Kumar Banyal, Rohitash Kumar Arora, Neeraj Department of Basic and Applied Sciences NIFTEM Kundli Sonepat131028 India Department of Computer Science and Engineering Rajasthan Technical University Kota324010 India School of Science and Technology Vardhman Mahaveer Open University Kota324010 India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Food safety is an important issue nowadays. Now, people become more aware of food safety and health concerns as their lifestyles have changed they follow more precautions for food consumption. The conventional technique of food safety is not capable of ensuring food safety very precisely and has many limitations so the food business operators are facing economic losses. To resolve these limitations, the researchers and scientists developed AI-based integrated machine learning technology. Machine learning is a computational science that enables learning, reasoning, and decision-making by interpreting and analyzing statistical patterns and subject data. ML tools like sensors, electric nose, and tongue, smart indicators, line-scan hyperspectral imaging systems, RT-PCR, ELISA, etc. are capable of overcoming these limitations of food safety practices by using the ML algorithms. The ML technique has great potential to overcome food safety issues and future applications in the food sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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Race Classification Using Deep Learning

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computers, Materials & Continua 2021年第9期68卷 3483-3498页

作者： Khalil Khan Rehan Ullah Khan Jehad Ali Irfan Uddin Sahib Khan Byeong-hee Roh Department of Information Technology and Computer Science Pak-Austria Institute of Applied Science and TechnologyHaripurPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwonKorea Department of Computer Science Superior CollegeLahorePakistan Department of Electronics and Telecommunications Politecnico di TorinoTorinoItaly

Race classification is a long-standing challenge in the field of face image *** investigation of salient facial features is an important task to avoid processing all face *** segmentation strongly benefits several face analysis tasks,including ethnicity and race *** propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation *** training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and *** DCNN model developed in the first phase was used to create segmentation *** probabilistic classification method is used,and probability maps(PMs)are created for each semantic *** investigated five salient facial features from among seven that help in race *** are extracted from the PMs of five classes,and a new model is trained based on the *** assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.

关键词： Deep learning facial feature face analysis learning race race classification

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Automation in Monitoring Temperature, Vibration of Industry and Preventing Accident Using IoT 3

Automation in Monitoring Temperature, Vibration of Industry ...

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Uma Maheswari, Sabari L Teotia, Shashiraj Kumaravel, R. Pandey, Himani Dhinakaran, K. Kumar, Ashok R.M.K. Engineering College Department of Electrical and Electronics Engineering Kavaraipettai India Swami Vivekanand Subharti University Department of Computer Application Meerut India Vivekanandha College of Engineering for Women Department of Computer Science and Technology Elayampalam India Baroda University Department of Applied Science and Humanities Vadodara India Misrimal Navajee Munoth Jain Engineering College Department of Computer Science and Engineering Chennai India Banasthali Vidyapith Department of Computer Science Rajasthan India

ISBN: (纸本)9798350336238

Industrial safety is more important in both safety of human being and machines. To ensure this the temperature of the machine is monitored continuously and if any abnormal situation arises, the machine will be isolated from the supply using IoT. Similarly, the sound and vibration inside the factory is monitored using vibration sensor and intimated to the control room if needed. The control room may be located in remote place the information will be sent to them using IoT. Vibration may induce due sudden fall of any heavy materials, braking of machine shaft, fall of objects from Crane, etc. The vibration caused by the unexpected incident will be identified by the vibration sensor provide in the factory/ Industry in various places. The sensor will alarm by siren and intimate to the operator or control room. SMS (Small Message Service) also send to the operator through GSM. © 2023 IEEE.

关键词： Accident prevention

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