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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques 8

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Jayanthi, L.N. Shanmugam, Kannan Suma, Challapalli Sagar, B.S. Maniraj, S.P. Ganeshbabu, T.R. Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Department of Gaming Technology Madhapradesh Bhopal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Faculty of Engineering & Technology Department of Data Science and Business Systems Chennai Tamil Nadu Kattankulathur India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350376425

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience. © 2024 IEEE.

关键词： Sentiment analysis

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Speech Detection and Comparison using Different Feature Extraction Method for Correct Verification 3

Speech Detection and Comparison using Different Feature Extr...

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3rd International Conference on communication, Control, and Intelligent systems, CCIS 2024

作者： Upadhyay, Shrikant Kumar, Binod Singh, Rajan Alam, Md. Irfan Kumari, Barkha Hinz, Binita Roshima Mlr Insitute of Technology Department of Electronics and Communication Engineeing Hyderabad India Jharkhand Rai University Facultyt of Cse & It Ranchi India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Jharkhand Rai University Faculty of Cse & It Ranchi India Pune India Rtc Institute of Technology Department of Computer Science and Engineering-Cyber Security Ranchi India

ISBN: (纸本)9798331528201

Mental status through physical appearance identification is not possible as it does not give complete status of any human. Using voice and considering quality extraction of talked it is quite easy to predict the mental behavior and internal variation that will help the doctor related with medical treatment that support them to give proper treatment to person. Hindi is the natural language of India and large community of population talk and speaks this language to exchange and communicate. Various inputs like specific feature, images and vital report of serious person and it is one of the important facts sources for deep training or gaining systems. In this research paper we try to identify the speech signal under different scenario like gun noise, car noise etc. following different feature extraction approaches and compare it for its proper verification. This verification may help to enhance the quality of speech in different adverse scenario and make the speech detection easy. Here, the comparison has been done with various feature extraction method and the results reflects that MFCC has good efficiency of 95% and with a low error rate of 2% compared to other approach. For analysis Hindi voice sample were used for the experimental analysis for 50 samples. © 2024 IEEE.

关键词： Speech enhancement

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Combining Convolutional Neural Networks with Reinforcement Learning for Autonomous Robotics

Combining Convolutional Neural Networks with Reinforcement L...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Padhiary, Mrutyunjay Powar, Swati Asokan, Anju Sundar Rajan, M. Al-Said, Khaleel Al Said, Nidal Assam University Triguna Sen School of Technology Department of Agricultural Engineering Assam Silchar788011 India Mit Adt University Computer Science Engineering Maharashtra Pune India Cse Nehru College of Engineering and Research Centre Kerala Thrissur India Arbaminch University Ethiopia Faculty of Electrical and Computer Engineering Arbaminch Institute of Technology India Middle East University Faculty of Arts and Educational Sciences Department of Educational Technology Amman Jordan Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

The use of artificial intelligence (AI) in robotics, particularly in the advancement of gaming robotics, has drawn a lot of interest as technological and scientific developments continue to advance. This innovation opens up new possibilities for creating smart autonomous robots for gaming. Nevertheless, there is more work to be done to achieve great accuracy and consistency in motion management. The challenges of the robotics' level of smart and the motion-capturing structure's capacity to prevent interruption still require resolution, although numerous methods have been put out to improve monitoring accuracy by merging various data. This study proposes an adaptable reinforcement learning (RL)-oriented multifaceted data combination (AdRL-MDC) system for training a robotic hand to play games alongside humans. It incorporates an adaptable training process for updating the ensemble classification algorithm, an RL paradigm that provides the robots with smart knowledge, and a multifaceted data combination framework that is resistant to interruption. The following studies demonstrate the AdRL-MDC system's previously indicated functionality. The ensemble framework, which combines a convolutional neural network (CNN), performs well in terms of accuracy and computing duration. Furthermore, the depth vision-oriented CNN classification algorithm achieves one hundred percent recognition accuracy, implying that the forecasted motions are the real value. © 2025 IEEE.

关键词： Autonomous robots Convolutional neural network Gaming Motion management Reinforcement Learning

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Optimized Spherical Network for Clustering based Fault-Tolerant and Reliable Routing in IoT-Enabled WSNs 8

Optimized Spherical Network for Clustering based Fault-Toler...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Asif, Md Prabha, M. Srinivasan, P. Santhosh Prabagaran, S. Jb Institute of Engineering and Tecnology Department of Electronics and Computer Engineering Moinabad Mandal Ranga Reddy District Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai India Karpagam Academy of Higher Education Faculty of Engineering Department of Mechanical Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376425

This research presents an innovative approach to optimize the performance and longevity of Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs). The proposed Osprey-Based Bowerbird Spherical Convolutional Network (OBSCN-GIA) framework addresses the critical challenges of energy consumption and fault tolerance. The OBSCN-GIA technique utilizes the Osprey Optimization Algorithm (OOA) for cluster head selection and cluster construction, ensuring efficient network organization. The Bowerbird Optimization Algorithm (BOA) is integrated to enhance fault tolerance and network survivability. A Spherical Convolutional Network (SCN)-based routing technique is employed to optimize WSN route selection, while the Geyser-Inspired Algorithm (GIA) fine-tunes SCN settings for optimal data transfer. The proposed approach was evaluated against existing models, demonstrating superior performance in terms of survivability, packet delivery ratio, network longevity, energy consumption, delay, and throughput. The OBSCN-GIA achieved a remarkable throughput of 99.94% and an average energy consumption rate of 0.02J for a network of 100 nodes. This research contributes to the development of more efficient and sustainable WSNs, paving the way for the widespread adoption of IoT technologies in various applications. © 2024 IEEE.

关键词： Fault tolerance

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An Efficient Fusion of Infrared and Visible Images Using an Optimized Deep Learning Model with Decomposed Non-subsampled Shearlet Structural Patch Transform

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New Generation Computing 2025年第3期43卷 1-27页

作者： Murugeswari, P. Rayen, Sonia Jenifer Ramkumar, M. Sakthi, U. Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Electronics and Communication Engineering Department Sri Krishna College of Engineering and Technology Coimbatore India Department of Computational Intelligence Faculty of Engineering & Technology SRM Institute of Science and Technology Chennai India

Multimodal data fusion is pivotal in artificial intelligence, with infrared and visible image fusion crucial for enhancing target detection. However, low-light visible images often lack vital details, impacting fusion quality and target detection accuracy, particularly in autonomous driving applications. To address these challenges, an innovative model is proposed that introduces a normalized depth-wise crested porcupine cross-attention model to extract complementary details from visible and infrared images. The incorporation of double cross-attention effectively enhances complementary information while minimizing redundant features. Subsequently, the Decomposed Non-subsampled Shearlet Structural Patch Transform (DNSSPT) model is employed to integrate features and generate the fused image. This process involves decomposing the extracted infrared and visible images into components such as signal intensity, mean intensity, and signal structure through structural patch decomposition. Next, a membership curve is applied to precisely determine the weight of the average intensity module, minimizing artifacts while preserving the importance of infrared targets. Additionally, sharpening operations are used to improve detail layer of both the visible and infrared images, leading to a fused image with higher contrast. Through subjective and objective evaluations, the proposed model outperforms existing fusion techniques, achieving minimum processing times of 0.0521, 0.0698, 0.0681, and 0.0832 s across the TNO, MSRS, VIFB, and VOT-RGBT datasets, respectively. Additionally, it attains high detection accuracies of 94% for VOT-RGBT, 92.71% for TNO, 96% for MSRS, and 90.89% for VIFB. Furthermore, object detection experiments using roadscene dataset confirm the DNSSPT model’s effectiveness in advancing computer vision tasks, achieving an accuracy of 94.1%.

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Identifying Inaccurate Stories: Leveraging High Knowledge and Big Data 15

Identifying Inaccurate Stories: Leveraging High Knowledge an...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Khan, Mohammad Ahmar Verma, Pushpendra Kumar Ghaldiyal, Shivangi Balaji, N.V. Francis, G. Arun Patil, Swati Bula Department of Management Information System College of Commerce & Business Administration Dhofar University Country Sultanate of Oman Oman School of Computer Science and Applications IIMT University Meerut India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India IT Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Research on fake news has gained significant traction in a number of fields, We describe how the issue is tackled from a natural language processing standpoint in this research with the aim of developing identify false material in news. Collecting high-quality data, such as examples of false and actual news stories on a diverse range of subjects, is the key obstacle in this field of study. We examine the available before introducing the site as our lab's gift to the . We provide the whole text of the news that have already been applied based on human evaluation of the articles' authenticity. In order to clarify the gaps and causes of imbalance in the datasets that are now accessible and to direct ongoing attempts, we also conduct an appearance-based experiment. We urge the public to gather more information and make it accessible for study. ©2024 IEEE.

关键词： Data collection

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Extending RIS Life Span for Reliable communication Under Hardware Ageing Effects

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the communications Society 2024年 5卷 3395-3409页

作者： Mondal, Atiquzzaman Ghosh, Shyamal Singh, Keshav Biswas, Sudip Duong, Trung Q. Indian Institute of Information Technology Guwahati Department of Electrical Communication Engineering Guwahati781015 India Indian Institute of Science Education and Research Thiruvananthapuram Department of Data Science Thiruvananthapuram695551 India National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS's life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments. © 2020 IEEE.

关键词： Wireless sensor networks

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Research on the quantum photonic convolutional neural network for artificial intelligence-based healthcare system security

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Optical and Quantum Electronics 2024年第2期56卷 1-18页

作者： Sita Kumari, K. Shivaprakash, G. Arslan, Farrukh Alsafarini, Maram Y. Ziyadullayevich, Avlokulov Anvar Haleem, Sulaima Lebbe Abdul Arumugam, Mahendran Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bengaluru India School of Electrical and Computer Engineering Purdue University West Lafayette United States Department of Electrical Engineering University of Engineering and Technology Lahore Lahore Pakistan Computer Science Department Zarqa University Zarqa Jordan The Department of Audit Tashkent Institute of Finance Tashkent Uzbekistan Department of Information and Communication Technology Faculty of Technology South Eastern University of Sri Lanka Oluvil Sri Lanka Center for Transdisciplinary Research Saveetha Dental College Saveetha Institute of Medical and Technical Science Chennai India

Artificial intelligence (AI) has quickly integrated into contemporary healthcare organizations, providing a variety of purposes, including identifying diseases, individualized therapy suggestions, and healthcare picture analytics. However, substantial security concerns are raised by the extensive use of AI in Healthcare, particularly regarding the reliability and confidentiality of private patient information. In this paper, we offer quantum photonic convolutional neural network (QP-CNN) to strengthen AI-based healthcare systems' security to overcome this crucial issue. To create an innovative convolutional neural network (CNN) architecture capable of handling the quantum computations that can endanger the safety of patient data, we analyze the special aspects of quantum photonics in this study. We explore the basic ideas behind quantum photonic computation and show how they can be used to develop strong cryptography methods for securing healthcare information during transport and preservation. Initially, we gather the dataset and preprocess the collected data using min–max normalization to remove duplicates and guarantee homogeneity. The subsequent stage involves utilizing principal component analysis (PCA) to extract pertinent features from the preprocessed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. In terms of accuracy (82.67%), precision (82%), recall (75.84%), and F1-score (85%), specificity (95.67%), AUC (93.15%). Our results show that the QP-CNN technique outperforms other methods in effectiveness. Our suggested QP-CNN technique provides exciting outcomes for the security of an artificial intelligence-based healthcare system. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Healthcare Patient evaluation Prediction Quantum photonic convolutional neural network (QP-CNN) Security

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Industry 4.0 Digital Technologies and Information systems: Implications for Manufacturing Firms Innovation Performance

Industry 4.0 Digital Technologies and Information Systems: I...

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2023 International Conference on computer communication and Informatics, ICCCI 2023

作者： Ayyasamy, Ramesh Kumar Shaikh, Farhan Bashir Lah, Nur Syadhila Binti Che Kalhoro, Shadab Chinnasamy, P. Krisnan, Saraswathy Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Kampar Kampar Malaysia

ISBN: (纸本)9798350348217

Cloud computing, big data analytics, and the internet of things are just a few of the recent technological marvels that have surprised organisations, particularly when it comes to managing innovation. The current study investigates the impact of the internet of things, big data analytics and cloud computing as digital capabilities on information systems and firms' innovation performance via a mediating role of dynamic and innovation capabilities. International entrepreneurial orientation has been taken as a moderator in this study. The study uses a quantitative method research approach conducting survey questionnaires from manufacturing SMEs in Malaysia. The conclusions of this study could aid the industry in identifying, assessing, and strengthening its skills in the ever-evolving technology trends, allowing them to produce new business models and innovate through information systems. People will have more awareness about necessary technological advancements and economic and job options as a result of their growth, and their social status will improve © 2023 IEEE.

关键词： Data Analytics

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