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applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： S Baghavathi Priya P V R Subba Rao TamilSelvi Madeswaran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences Nizwa Oman

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Recent developments in the area of research on sign language recognition have led to making use of deep learning techniques with the goal to assist the self-learning transition of sign language from one language to another. In order to detect and classify characters from photographs in multiple sign languages, a number of deep-learning models, have been deployed. SLR is a prominent area of study and technology that aims to mitigate speaking barriers for individuals who unable to talk and listen. This involves the development of tools capable of understanding and deciphering sign language movements, hence facilitating a wider range of interactions for those who are difficult of listening and speaking. This paper demonstrates a novel model called CNN Inception Net, which is a neural network based on convolution. The use of an Indian sign dataset enables the achievement of a notable performance rate of 98.9%, enabling precise recognition of actions in the language of signs.

关键词： Deep learning Sign language Accuracy Convolution Neural networks Assistive technologies Data augmentation

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Optimal Logistic Map with DNA computing Model for Image Encryption 2

Optimal Logistic Map with DNA Computing Model for Image Encr...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Shiny, K.V. Wilfred Blessing, N.R. Jino, R. Palarimath, Suresh Mathumitha, V. Sutherlin Subitha, G. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman Department of Civil Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil nadu Kanyakumari District India

ISBN: (纸本)9798331542573

The logistic map with optimization with a novel Deoxyribonucleic Acid (DNA) sequence operation-based novel image encryption scheme is generated. The best mask is obtained by enhancing the excellence of DNA which is the significant advantage of this approach. According to the image decryption and encryption process, correct errors and the similarity of DNA sequences are decreased by the DNA cryptogram becoming an input pace for DNA calculation. The maximum PSNR of encrypted images is obtained by the logistic map function to improve the performance of image security Ant Lion optimization (ALO). The image encryption and decryption process generate optima point DNA sequence rules. DNA coding: utilizing the complementary rule, each nucleotide is then changed into its base pair for a random time or times;the times are produced by Chebyshev maps. The experimental investigations provided better UACI, Number of Pixel Exchange Rate (NPCR), and Peak Signal Noise Ratio (PSNR). This strategy is significant because it increases entropy, which is the fundamental property of randomness, withstands multiple statistical and differential attacks, and produces positive experimental outcomes. © 2024 IEEE.

关键词： Signal to noise ratio

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

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Computer Systems Science & Engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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RDD4D: 4D Attention-Guided Road Damage Detection And Classification

arXiv

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arXiv 2025年

作者： Alkalbani, Asma Saqib, Muhammad Alrawahi, Ahmed Salim Anwar, Abbas Adak, Chandarnath Anwar, Saeed The Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri 516 Oman NCMI CSIRO The University of Technology Sydney Australia FutureDataMinds Indian Institute of Technology Patna India The School of Computing Australian National University Australia

Road damage detection and assessment are crucial components of infrastructure maintenance. However, current methods often struggle with detecting multiple types of road damage in a single image, particularly at varying scales. This is due to the lack of road datasets with various damage types having varying scales. To overcome this deficiency, first, we present a novel dataset called Diverse Road Damage Dataset (DRDD) for road damage detection that captures the diverse road damage types in individual images, addressing a crucial gap in existing datasets. Then, we provide our model, RDD4D, that exploits Attention4D blocks, enabling better feature refinement across multiple scales. The Attention4D module processes feature maps through an attention mechanism combining positional encoding and "Talking Head" components to capture local and global contextual information. In our comprehensive experimental analysis comparing various state-of-the-art models on our proposed, our enhanced model demonstrated superior performance in detecting large-sized road cracks with an Average Precision (AP) of 0.458 and maintained competitive performance with an overall AP of 0.445. Moreover, we also provide results on the CrackTinyNet dataset;our model achieved around a 0.21 increase in performance. The code, model weights, dataset, and our results are available on https://***/msaqib17/Road_Damage_Detection. © 2025, CC BY.

关键词： Damage detection

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Effective Surrogate Models for Docking Scores Prediction of Candidate Drug Molecules on SARS-CoV-2 Protein Targets

Effective Surrogate Models for Docking Scores Prediction of ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shi, Wenqi Murakoso, Mio Guo, Xiaoyan Xiong, Linxi Chen, Matthew Wang, May D. Georgia Institute of Technology Department of Electrical and Computer Engineering Atlanta United States Kyoto University Department of Applied Life Science Kyoto Japan Georgia Institute of Technology College of Computing Atlanta United States Tsinghua University Tanwei College Beijing China Georgia Institute of Technology Wallace H. Coulter Department of Biomedical Engineering Atlanta United States

ISBN: (纸本)9798350337488

Emerging infectious diseases, such as coronavirus disease 2019 (COVID-19), pose a major threat to public health and present a critical challenge for drug discovery. Due to the cost- and time-consuming process of new drug development, virtual pre-screening methods such as protein-ligand docking prediction have become essential tools in enhancing drug refurbishment and repurposing. In this study, we propose a machine learning-based surrogate model for docking score prediction of drug candidates on SARS-CoV-2 protein targets via deep feature concatenation. We investigate 14 different combinations of rule-based and data-driven fingerprinting methods to identify the optimal representation of candidate drug molecules. Extensive experiments on docking scores of 270,000 molecules across 18 different SARS-CoV-2 protein targets demonstrate the effectiveness of the proposed surrogate models. In addition to unseen drugs, we further investigate the generalization of the proposed framework for unseen protein targets. This study may provide an instrumental and generalizable framework for exploring ligand-protein interaction, serving as a useful tool to facilitate rapid drug pre-screening during emerging public health crises. © 2023 IEEE.

关键词： COVID-19 drug target explainable artificial intelligence feature concatenation interaction prediction neural networks

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Smart Switch technology: IoT Integration with Touch Control for Improved Accessibility 1

Smart Switch Technology: IoT Integration with Touch Control ...

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1st International Conference on Innovative Engineering Sciences and Technological Research, ICIESTR 2024

作者： Baloch, Farah Khaliq Abro, Ghulam E. Mustafa Alansari, Zainab Hitec University Electrical Engineering Department Taxila Cantt Rawalpindi Pakistan King Fahd University of Petroleum & Minerals Interdisciplinary Research Centre for Aviation and Space Exploration Dhahran Saudi Arabia University of Technology and Applied Sceicnes College of Computing and Information Sciences Muscat Oman

ISBN: (纸本)9798350348637

The Internet of Things (IoT) has ushered in an era of transformative innovation, revolutionizing the way we interact with our surroundings. In the context of home automation, the IoT-Based Smart Switch with Touch Control project represents a leap forward in convenience and efficiency. This research explores the development and implementation of a switch board devoid of mechanical components, relying instead on touch sensors and advanced microcontroller technology. The aim is to create a touch-operated switchboard, of standard size and uncompromising reliability, with the potential to revolutionize everyday living. The research delves into the methodology, key components, beneficiaries, and the constraints involved in this innovative project. Moreover, it provides a detailed account of the project's first evaluation task and the technology used, including the PZEM Power Module, ESP8266 NodeMCU, and Home Assistant. This research illuminates the potential of IoT in simplifying and enhancing the lives of not only the technologically inclined but also senior citizens and the middle class, who may have been previously unable to afford high-cost switch boards with advanced functionality. © 2024 IEEE.

关键词： Microcontrollers

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Deep Learning Scheme for Modulation Classification under Fading Channels 7

Deep Learning Scheme for Modulation Classification under Fad...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Ghauri, Sajjad A. Sarfraz, Mubashar Chhabra, Gunjan Kaushik, Keshav Zafar, Asim Khan, Inam Ullah Isra University School of Engineering & Applied Sciences Islamabad Pakistan Numl University Faculty of Engineering and Computing Islamabad Pakistan Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Amity University Amity School of Engineering and Technology Punjab India National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350350067

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture's performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats. © 2024 IEEE.

关键词： Signal modulation

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Exsolution of CoFe(Ru)nanoparticles in Ru-doped(La_(0.8)Sr_(0.2))_(0.9)Co_(0.1)Fe_(0.8)Ru_(0.1)O_(3−δ)for efficient oxygen evolution reaction

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Nano Research 2022年第8期15卷 6977-6986页

作者： Yi Liang Yu Cui Yang Chao Ning Han Jaka Sunarso Ping Liang Xin He Chi Zhang Shaomin Liu School of Applied Physics and Materials Wuyi UniversityJiangmen 529020China Department of Materials Engineering KU LeuvenLeuven 3001Belgium Research Centre for Sustainable Technologies Faculty of EngineeringComputing and ScienceSwinburne University of TechnologyJalan Simpang TigaKuching 93350SarawakMalaysia College of Chemical Engineering Beijing University of Chemical TechnologyBeijing 100029China

The rational modification of perovskite oxides(ABO3−δ)is essential to improve the efficiency and stability of oxygen *** engineering represents a facile approach to modify perovskites for enhanced *** compositional design and in situ exsolution,a Ru-doped(La_(0.8)Sr_(0.2))_(0.9)Co_(0.1)Fe_(0.8)Ru_(0.1)O_(3−δ)(LSCFR)perovskite anchored with CoFe(Ru)alloy particles on the surface was fabricated for oxygen evolution reaction(OER)in this *** results and calculations indicate that Ru-doping promotes the exsolution of CoFe(Ru)from the perovskite *** exsolution in the reduced atmosphere for 3 h,the catalyst(LSCFR-3)exhibited superior OER performance with an overpotential of 347 mV and a Tafel slope of 54.65 mV·dec^(−1),and showed good stability in contrast to the pristine *** exsolution of CoFe(Ru)particles,Ru doping,and the increase of surface oxygen vacancies are responsible for the enhancement of OER *** findings obtained in this study highlight the possibility of controlling exsolution and composition of nanoparticles by element doping and prove that in situ exsolution is an effective strategy for designing OER catalysts.

关键词： perovskite oxides in situ exsolution oxygen evolution reactions CoFe nanoparticles B-site cation doping

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Health Data Analytics Using IoT: Transforming Healthcare Management and Treatment Strategies for Improved Patient Outcomes

Health Data Analytics Using IoT: Transforming Healthcare Man...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Tuan, Dinh Cong Unnikrishnan, Paravthy Dhandayuthapani, Bala Vennila, M.E.M. Long, Choi Sang Sahoo, Deepali Rani Dai Nam University Faculty of Communication Viet Nam Amritapuri690525 India College of Computing and Information Sciences University of Technology and Applied Sciences Department of It Shinas Campus Oman Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Chengalpattu India Raffles University Faculty of Business Malaysia Symbiosis Law School Noida India Pune India

ISBN: (纸本)9798331543617

The integration of Web of Things (IoT) innovation within the healthcare segment has revolutionized conventional healthcare administration and treatment procedures, driving progressed quiet results. This paper looks at the transformative effect of well-being information analytics utilizing IoT in upgrading healthcare administrations, optimizing understanding care, and advancing preventive measures. The paper analyzes the key components and applications of IoT in healthcare, emphasizing its part in information collection, real-time observation, and examination of understanding imperative signs, action levels, and pharmaceutical adherence. With the help of IoT-enabled gadgets such as wearables, sensors, and keen checking frameworks, healthcare experts can assemble comprehensive, real-time persistent information, encouraging early discovery and opportune mediation for basic well-being issues. Besides, the utilization of IoT in well-being information analytics empowers the improvement of personalized treatment plans custom-fitted to a person's persistent needs, cultivating a patient-centric approach to healthcare conveyance. By leveraging machine learning calculations and prescient analytics, healthcare suppliers can expect potential well-being complications, in this manner minimizing the chance of antagonistic occasions and clinic readmissions. In addition, the execution of IoT-based healthcare arrangements advances further understanding and observing, empowering healthcare suppliers to offer ceaseless, personalized care past conventional healthcare settings. © 2024 IEEE.

关键词： Patient treatment

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