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Integrated Circuits and communication systems (ICICACS), IEEE International Conference on

作者： K. Meenakshi Bathini Ravinder Mohammed Rehaman Pasha K. Thinakaran Dabbara Jayanayudu S.P. Maniraj Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Siddartha Institute of Science and Technology Puttur Andhra Pradesh India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer’s patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand.

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Efficient Recognition of Complex Human Activities Based on Smartwatch Sensors Using Deep Pyramidal Residual Network 15

Efficient Recognition of Complex Human Activities Based on S...

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15th International Conference on Information Technology and Electrical engineering, ICITEE 2023

作者： Mekruksavanich, Sakorn 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 Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350304466

Human activity recognition (HAR) has become a hot topic in artificial intelligence research due to the rapid development of smart wearable technologies. The goal of HAR is to accurately identify human actions using various data sources, such as video, images, and sensor data from wearable devices. Recent research in HAR has achieved promising results using learning-based methods, especially deep learning techniques. However, achieving state-of-the-art results remains a challenge for researchers. This study proposes a new approach to HAR that uses deep learning to classify human activities from smartwatch sensor data. We propose the use of a one-dimensional deep pyramidal residual network (1D-PyramidNet) for accurate human action identification. We evaluate the performance of our model against baseline models using the DHA dataset, a benchmark dataset for HAR that includes wristwatch sensor data for 11 complex human activities. The experimental results show that our 1D-PyramidNet model outperforms the baseline models, including CNN, LSTM, BiLSTM, GRU, and BiGRU. This confirms that the use of 1D-PyramidNet can improve the identification capabilities of HAR systems, achieving a maximum accuracy of 96.64%. © 2023 IEEE.

关键词： Wearable sensors

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Attention Based Hybrid Deep Learning Network for Locomotive Mode Recognition in Natural Environments Using Wearable Sensors 4

Attention Based Hybrid Deep Learning Network for Locomotive ...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

The field of pervasive computing encompasses the significant domain of study known as WS-LMR, which focuses on identifying locomotive modes through wearable sensors. WS-LMR aims to automatically evaluate and interpret individual locomotive actions along with contextual information using wearable sensor data. This technology finds applications in medical surveillance systems and various systems involving interactions between individuals and intelligent wearable devices. However, existing WS-LMR algorithms are often evaluated using data collected under controlled conditions, limiting their effectiveness in uncontrolled real-world environments. This scholarly article addresses the primary objective of identifying human locomotion activities in real-world contexts. To enhance the efficacy of the WS-LMR system, our study proposes the utilization of an attention-based hybrid deep learning network that combines convolutional neural network and long-short term memory networks, referred to as the Att-CNN-LSTM model. We evaluated the performance of the proposed network using the publicly available Bath Natural Environment HAR dataset, a benchmark LMR dataset. Both model training and testing were conducted using a 5-fold cross-validation approach. The Att-CNN-LSTM model achieved the highest accuracy of 94.25% and an F1-score of 91.47% based on the results obtained from multiple experiments. ©2023 IEEE.

关键词： Benchmarking

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Time Series Classification Using Deep Learning for HAR Based on Smart Wearable Sensors 26

Time Series Classification Using Deep Learning for HAR Based...

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26th International computer science and engineering Conference, ICSEC 2022

作者： Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering 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)9781665491983

In the last decades, time series classification (TSC) has emerged as one of the most challenging issues in data mining, and extensive studies have been done on various methods, including algorithm-based and learning-based techniques. Sensor-based human activity recognition (HAR) is a TSC issue that has become one of the most sought-after fields among business and academia specialists because of the proliferation of smartphone technology and wearable movement sensors. Conventional approaches to feature extraction provide a significant challenge in feature selection. Deep learning is an efficient strategy in the HAR scientific field and has solved the issue of feature selection. Nevertheless, several obstacles remain to study topics, including classifier interpretation. This article integrates well-known deep learning methods, namely convolutional neural networks and RNN-based models. The new approach proved to be more effective than the existing state-of-the-art approach. We assessed our network on the multivariant time-series benchmark (UCI-HAR) and revealed that our model surpasses other models in terms of training time and overall accuracy. © 2022 IEEE.

关键词： Wearable sensors

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Adaptive Intrusion Detection in Cyber-Physical systems Using Reinforcement Learning-Based Autoencoders

Adaptive Intrusion Detection in Cyber-Physical Systems Using...

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Integrated Intelligence and communication systems (ICIICS), International Conference on

作者： N. Rajathi Divya. S s. Anitha Elavarasi G. Saritha V. Jeya Ramya Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments.

关键词： Adaptation models Adaptive systems Accuracy Autoencoders Intrusion detection Reinforcement learning Cyber-physical systems Real-time systems Critical infrastructure Anomaly detection

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AI Dynamics: Revolutionizing Organizational Skills and Contributing to Enhanced Business in Morocco 9th

AI Dynamics: Revolutionizing Organizational Skills and Contr...

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9th International Conference on Smart City Applications, SCA 2024

作者： Chegri, Badre Eddine Aiche, Karim Berhich, Asmae Mettiti, Abderrahman El Rhajbal, Zineb Saadane, Rachid Chehri, Abdellah Mohammed V University in Rabat Rabat Morocco AMIPS Research Team E3S Research Center Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohammed V Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

This study investigates the transformative effects of Artificial Intelligence (AI) on organizational skills within the Moroccan context. Utilizing a mixed-methods approach, combining qualitative interviews and a quantitative survey across diverse industries, the research explores AI's impact on crucial competencies like adaptability, communication, decision-making, and collaboration. The findings reveal a nuanced picture of both positive and challenging aspects of AI adoption, emphasizing the need for strategic planning and skill development. Focused on Morocco, this research contributes unique insights into the socio-economic implications of AI in a developing economy. The study's outcomes provide actionable recommendations for policymakers, business leaders, and educators, highlighting the imperative of fostering an environment that maximizes the potential of AI for organizational skills enhancement, ultimately bolstering overall competitiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Strategic planning

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A Comparative Study of Deep Learning Robustness for Sensor-based Human Activity Recognition 46

A Comparative Study of Deep Learning Robustness for Sensor-b...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Recognition of human activities has become a highly significant area of research in the current era of the Internet of Things and A.I. technologies. Human activity recognition (HAR) plays a crucial role in monitoring and analyzing human behaviors using various types of sensors. Such sensor-based HAR systems are being used in various industry domains, including healthcare, professional sports, and worker assessment in intelligent factories. The integration of micro electronics mechanical systems sensor technology in smart wearable devices has significantly improved the capabilities of HAR using machine learning and deep learning techniques. However, processing longtime dependent sequence samples with noisy data is still a significant challenge, affecting the classification time and accuracy. This study aimed to analyze noise-tolerant deep learning models for recognizing physical activities from noisy smartphone data. The study analyzed the recognition performances of the five most popular deep learning networks for HAR under different levels of Gaussian noise. The results indicated that the BiGRU network demonstrated outstanding robustness to Gaussian noise of different levels. © 2023 IEEE.

关键词： Smartphones

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Estimation of Bandwidth Requirement for Underwater Fiber Optic Networks

Estimation of Bandwidth Requirement for Underwater Fiber Opt...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Choudhary, Sanjay Sahoo, Girija Shankar Rajesh, A. Khurana, Jatin Yuvaraj, S. Shewale, Chaitali R. Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350329773

This paper affords an estimation of the bandwidth requirement for underwater fiber optic networks by thinking about transmission c together with signal attenuation, dispersion, and nonlinear effects (NLEs). The authors present an evaluation of the signal power level and the propagation of the signal in an underwater environment. From this evaluation, they obtain empirical expressions for the desired signal-to-noise ratio (SNR) to achieve a given bit-errors charge (BER) for different sorts of fiber optics. The impact of NLEs at the sign excellent is also investigated by means of modeling these effects with polynomial expressions under high electricity excitation of the optical fiber. The authors suggest numerous methods for estimating the bit fee required for a given data rate and BER, inclusive of the p-endurance method, which is based totally on an assessment of the energy of the received optical sign. The results acquired from this observation suggest that NLEs can extensively affect the overall performance of optical communications, leading to extra design concerns and extended machine requirements for underwater fiber optic networks. © 2023 IEEE.

关键词： Signal to noise ratio

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Deep Residual Neural Network for Aggressive Physical Activity Recognition Using Surface Electromyography Sensors 14

Deep Residual Neural Network for Aggressive Physical Activit...

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14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Mekruksavanich, Sakorn 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 Intclligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350336269

Wearable sensors and machine learning have opened new doors for innovative automated systems. Smart wearables like smartwatches and wristbands can efficiently record human movements due to their integrated, compact sensors. This data collection is vital for understanding human behavior, a field known as human activity recognition (HAR), dedicated to classifying actions. Deep learning methods, which autonomously extract intricate features, have shown promise in HAR. Sensor-based HAR is extensively studied in academia and applied across domains like wellness tracking, medical diagnosis, and mobility analysis. However, challenges persist in detecting complex activities. Our study focuses on recognizing aggressive behaviors through surface electromyography (sEMG) sensor data analysis. We introduce ResNeXt, a novel deep neural network, to improve recognition accuracy. Through a series of tests, we evaluate ResNeXt's recognition capabilities and compare its results to other deep learning models. Our experiments reveal that ResNeXt outperforms other models, achieving an impressive 94.06% overall accuracy and an exceptional 97.68% accuracy in distinguishing normal from aggressive actions. © 2023 IEEE.

关键词： Wearable sensors

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Smart Energy Management system Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management system(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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