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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Kumara, Bharath Barmola, Pareshwar Prasad Arora, Renuka Parikh, Swapnil M Maranan, Ramya Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Parul Institute of Engineering and Technology Parul University Faculty of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices in real-time, the suggested method makes use of virtual twin technology that is combined with machine learning techniques. Automated response generation, continuous threat assessment, and anomaly detection are made possible by creating a digital clone of the actual IoT network using the virtual twin paradigm. By combining deep learning models like CNNs and LSTMs, it becomes easier to forecast possible security risks in network traffic data by seeing intricate spatial and temporal patterns. When tested in a virtual Internet of Things (IoT) setting, the suggested framework outperforms conventional rule-based approaches by a wide margin, reducing reaction time by 35% while achieving an accuracy of 9 6. 2 %in threat identification. © 2024 IEEE.

关键词： Risk assessment

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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An energy-aware scheme for solving the routing problem in the internet of things based on jaya and flower pollination algorithms

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11363-11372页

作者： Sadrishojaei, Mahyar Navimipour, Nima Jafari Reshadi, Midia Hosseinzadeh, Mehdi Tehran Iran Department of Computer Engineering Kadir Has University Faculty of Engineering and Natural Sciences Istanbul Turkey Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Institute of Research and Development Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam

Clustering and routing protocols for Internet of Things (IoT) need to consider energy usage and how to reduce it. Unbalanced power usage is a common concern with current solutions to cluster-based routing problems in the IoT ecosystem. This research developed a swarm intelligence-based clustering technique to achieve a more uniform dispersion of cluster heads. The data packets across cluster heads and the sink are routed via a Jaya algorithm. Based on average remaining energy, number of active nodes, number of nodes that have failed or have been removed from the network, and overall network throughput, this combined clustering and routing method's quality has been assessed. The integrative clustering and routing protocol based on the flower pollination algorithm and Jaya algorithm described here exhibit considerable improvements over the current state-of-the-art. The network throughput and the number of the alive node are essential statistics for evaluating IoT in which battery-powered devices periodically acquire surroundings data and transmit gathered samples to a base station. The proposed strategy improved network throughput and the number of dead nodes by at least 14% and 18%, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Fabrication, Simulation and Comparison of Three Types of Polymer-based, Perovskite and Dyesensitized Solar Cells 4

Fabrication, Simulation and Comparison of Three Types of Pol...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Yahagh, Majid Boroumand, Farhad A. K.N. Toosi University of Technology Organic Electronics Lab Electronics Group Faculty of Electrical and Computer Engineering Tehran Iran K.N. Toosi University of Technology Organic Electronics Lab Electronics Group Materonics and Materionics Research Group Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350395914

In this study, three types of polymer-based, perovskite and dye-sensitized solar cells have been fabricated and simulated. Then, these three structures are compared in terms of output parameters such as efficiency, fill factor, fabrication methods and etc. In the fabrication of the polymer-based solar cell with Al / MEH-PPV:ZnO np / PEDOT:PSS / ITO structure, in the perovskite solar cell with Au / Spiro-OMeTAD / CH3NH3PbI3 / TiO2 / FTO structure and for the dye-sensitized solar cell with Pt / Electrolyte/ TiO2-N719 / FTO structure by the spin-coating and Dr. Blade method is used. The simulation of these three structures has been done using two software, Comsol Multiphysics and Silvaco. According to the results of experimental characterizations, the efficiency for the polymer-based solar cell is ηexp = 0.74%, the perovskite ηexp=8.66% and for the dye-sensitized ηexp=6.07%. Also, according to the simulation results, the efficiency for the polymer-based solar cell is ηcomsol=1.1%ηsilvaco=0.73%, the perovskite ηcomsol = 6.06%ηsilvaco = 8.6% and for the dye-sensitized ηcomsol=1.4%ηsilvaco=1.74%. According to the results of this study, the Perovskite solar cell is the most suitable cell in terms of efficiency and, of course, has a higher manufacturing cost than the other two types. © 2024 IEEE.

关键词： Perovskite

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Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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A Model-Driven Approach for Automatic Generation of Android Tourism Applications 13

A Model-Driven Approach for Automatic Generation of Android ...

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13th International Conference on Information and Knowledge Technology, IKT 2022

作者： Adib, Sara Zamani, Bahman Mdse Research Group University of Isfahan Faculty of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350320626

Developing mobile applications in general, and Android tourism applications in particular, is a laborious and challenging task. This problem has roots in 1) the required domain knowledge and 2) the diversity and fast emerging requirements of a tourism app. To address these issues, in this paper, we propose a novel approach based on model-driven engineering (MDE) principles. To make the approach practical, it is supported by three contributions: a domain-specific modeling language (DSML), a graphical editor, and a transformation engine. To build an app using our approach, the developer must first design the model of the app using the graphical editor, and then generate the app code, automatically using the transformation engine. To evaluate the proposed approach, we built a tourism app as a case study and showed that the proposed approach expedites the development process 8 times compared to the traditional programming. © 2022 IEEE.

关键词： Modeling languages

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Publication performance and trends in virtual reality research in education fields: a bibliometric analysis

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Multimedia Tools and Applications 2025年第10期84卷 6999-7026页

作者： Elaish, Monther M. Yadegaridehkordi, Elaheh Ho, Yuh-Shan Computer Science Department Information Technology Faculty University of Benghazi Benghazi Libya College of Information and Communications Technology School of Engineering and Technology CQUniversity Sydney Australia Trend Research Centre Asia University No. 500 Lioufeng Road Taichung41354 Taiwan

Virtual Reality (VR) technology has the potential to enhance education by providing immersive and engaging learning experiences that can improve teaching and learning outcomes. While there is a growing interest in utilizing VR in education settings, further research is needed to understand its pedagogical effectiveness and address associated considerations and challenges. This bibliometric study comprehensively analyzes 1,157 relevant articles from the Social Science Citation Index (SSCI) and the Science Citation Index Expanded (SCI-EPANDED) to gain insights into the current state of VR integration in education. The analysis revealed variation in VR adoption and research output across countries and institutions, underscoring the importance of collaboration and knowledge-sharing in the field. Key research areas and trends, such as the use of VR for skill development and training, were identified. Additionally, the study highlighted the need for more research on the pedagogical effectiveness of VR. The findings carry practical implications for guiding future research shaping policy decisions and advocating for a concerted effort to harness VR’s capabilities in education. This study serves as a practical roadmap, promoting the implementation and improvement of VR technology and fostering equitable and inclusive educational practices. © The Author(s) 2024.

关键词： Virtual reality

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Performance of Gradient-Based Optimizer for Optimum Wind Cube Design

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computers, Materials & Continua 2022年第4期71卷 339-353页

作者： Alaa A.K.Ismaeel Essam H.Houssein Amir Y.Hassan Mokhtar Said Faculty of Computer Studies(FCS) Arab Open University(AOU)Muscat130Sultanate of Oman Faculty of Science Minia UniversityMiniaEgypt Faculty of Computers and Information Minia UniversityMiniaEgypt Electronics Research Institute GizaEgypt Electrical Engineering Department Faculty of EngineeringFayoum UniversityFayoumEgypt

Renewable energy is a safe and limitless energy source that can be utilized for heating,cooling,and other *** energy is one of the most important renewable energy *** fluctuation of wind turbines occurs due to variation of wind velocity.A wind cube is used to decrease power fluctuation and increase the wind turbine’s *** optimum design for a wind cube is the main contribution of this *** decisive design parameters used to optimize the wind cube are its inner and outer radius,the roughness factor,and the height of the wind turbine hub.A Gradient-Based Optimizer(GBO)is used as a new metaheuristic algorithm in this *** objective function of this research includes two parts:the first part is to minimize the probability of generated energy loss,and the second is to minimize the cost of the wind turbine and wind *** Gradient-Based Optimizer(GBO)is applied to optimize the variables of two wind turbine types and the design of the wind *** metrological data of the Red Sea governorate of Egypt is used as a case study for this *** on the results,the optimum design of a wind cube is achieved,and an improvement in energy produced from the wind turbine with a wind cube will be compared with energy generated without a wind *** energy generated from a wind turbine with the optimized cube is more than 20 times that of a wind turbine without a wind cube for all cases studied.

关键词： Wind turbine wind cube gradient-based optimizer metaheuristics energy source

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