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2024 International Conference on Optimization Computing and Wireless communication, ICOCWC 2024

作者： Nishat, Humaira Balaji, T.S. Shargunam, S. Sasikala, R. Rani, K. Cvr College of Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai600026 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Karpagam College of Engineering Department of Cyber Security Tamilnadu Coimbatore641032 India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India

ISBN: (纸本)9798350383348

The data collected by the authorised sensor devices is transferred to the cloud for safe storage. communication occurs via a secure key management system designed for a decentralised network. Subsequently, it is important to guarantee that only authorised individuals are able to get this data from the cloud. Ensuring user authentication is of utmost importance in resource-constrained IoT devices due to the large number of users accessing sensitive data. The current user authentication methods suffer from accuracy, error rate, and implementation time problems. A novel solution is offered to tackle these challenges by introducing a sophisticated stacked ensemble authentication architecture that utilises cellphones for IoT systems. The suggested authentication approach utilises gait analysis to authenticate individuals by recognising their unique patterns of human movement. A smartphone equipped with an accelerometer and gyroscope sensor is used to gather gait characteristics. The proposed system has two components: preprocessors and classifiers. The preprocessor performs noise reduction and feature extraction. Subsequently, stacked ensemble machine learning classification models, including base and meta classifiers, are used to classify the characteristics, such as linear acceleration, rotation rate, and gravity, for the purpose of user authentication. The accuracy and error rate of the outcomes are compared with the schemes that already exist. The observation indicates that the suggested framework attains a 95% accuracy rate, a lower error rate, and optimised execution time. © 2024 IEEE.

关键词： GAIT Authentication Internet of Things (IoT) KNN

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engineering Students' Attitudes and Perceptions towards Project-Based Learning: A Study from Kerala, Southern India 13

Engineering Students' Attitudes and Perceptions towards Proj...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Akshay, R.S. Sunny, Alviya Saranya, V.S. John, Aby Subheesh, N.P. Anzar, S.M. Government Engineering College Department of Electronics and Communication Engineering Trivandrum India Model Engineering College Department of Computer Science and Engineering India Accenture Solutions Pvt Ltd Coimbatore India Rudn University Faculty of Philology Moscow Russia Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India

ISBN: (纸本)9798350376234

The shift from teacher-centric to student-centric education has been a subject of debate, with traditional instruction often criticized for fostering rote learning and passive engagement. Due to its emphasis on student autonomy and real-world application of knowledge, the growing popularity of Project-Based Learning as a novel alternative to traditional classroom-based teaching pedagogy is attracting educational institutions all around the world. In engineering education, multifaceted skill development is paramount. PjBL offers a dynamic approach to cultivate essential competencies and it significantly improves engineering education. The National Education Policy 2020 (NEP 2020) of India lays a strong emphasis on Project-Based Learning as the cornerstone of educational reform. But, only smaller number of research studies are conducted in the country to explore the attitudes and perceptions of students towards Project-Based Learning in the engineering sector. It lays bare the imminent need for educationalists to focus on this research gap. This paper explores the attitudes and perceptions of engineering students towards Project-Based Learning from Kerala, a state in Southern India. A novel questionnaire entitled Project-Based Learning in engineering Education in Kerala (PLEEK) is developed by the IEEE Education Society Kerala Chapter to conduct the survey. A total of 136 students from all over Kerala attended the survey. It is observed that the students have a positive perception of Project-Based Learning and its impact on enhancing their education. On the other hand, an abysmally low count of 11% of students believed that their current campus ecosystem supported this teaching pedagogy. They highlighted the need for a drastic transformation in the present academic infrastructure to support Project-Based Learning. The findings of the study underscore the potential of Project-Based Learning in enhancing learning outcomes, fostering critical skills, and preparing studen

关键词： engineering education

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Architecture-based Evaluation of VGG16 and ResNet Models for an Online Deep Learning Environment for Medical Applications 3

Architecture-based Evaluation of VGG16 and ResNet Models for...

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3rd International Conference on Innovations in science and Technology for Sustainable Development, ICISTSD 2022

作者： Chilakalapudi, Hari Priyanka Venkatesan, Ramya Kamatham, Yedukondalu Annamalai University Faculty of Engineering and Technology Department of Computer Science and Engineering Chidambaram India Matrusri Engineering College Department of Computer Science and Engineering Hyderabad India CVR College of Engineering Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9781665499361

Image classification using deep learning models has evolved impressively well in the past decade. Datasets containing millions of images grouped into thousands of classes have been used to train and test these models. Medical image classification however still faces the challenge of scarcity in datasets. Gathering data from various locations and placing it in a commonly accessed dataset is highly time-consuming. Diseases need real-Time response just like any other mission-critical operation and online deep learning could be handy. There are many pre-Trained models which acquired good accuracy on large datasets. But as the depth of the model increases the time taken to train the model and the number of computations also increase. In this paper, we evaluated two models with different architectures. VGG16 is a 16-layer normal stack of convolutional layers and ResNet50V2 is a stack of residual blocks with skip connections and 50 layers. We used a computer Tomography (CT) Lung image dataset to classify images into COVID, healthy and pneumonia images. We found that VGG16 is taking lesser time and computations with reduced loss when compared to the ResNet50V2 model. We finally conclude that ResNet50V2 is taking more time to train images as the model is 50 layers deep, whereas the VGG16 model is only 16 layers deep. Also, images that show mild infection were predicted as healthy images by ResNet50V2 but predicted correctly by the VGG16 model. © 2022 IEEE.

关键词： COVID-19

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An optimized MCHVD approach to curtail handover failures in wireless networks

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1989-1995页

作者： Gupta, Sachin Deepak, Shashikant Bhuvana, J. Singh, Vaishali Jindal, Kanika Vekariya, Vipul Chandrasekhar, Malepati Narula, Gagandeep Singh Department of Management Sanskriti University Uttar Pradesh Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bengaluru India Maharishi School of Engineering and Technology MUIT Lucknow India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Noida India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Bengaluru India Guru Gobind Singh Indraprastha University Delhi Dwarka India

The applications of wireless local area networks (WLAN’s) are highly accepted by mobile terminals (MT’s) due to their high transmission bandwidth and reduced costs. However, in such high speed networking platforms, there is a possibility of unnecessary handover failure (UHF) due to limited coverage of WLAN’s. Therefore, the given paper provides a multi-criteria hysteresis vertical handover decision (MCHVD) approach for wireless systems in order to minimize handover failure by analysing and validating its performance with conventional received signal strength (RSS) scheme of source and target cells. Using OPNET simulator, the proposed approach is tested over 60 samples, which are divided equally among networks such as Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), and Universal Mobile Telecommunication System (UMTS) based on parameters like G power as 0.8, threshold energy limit as 0.05%, and confidence interval as 95%, thereby keeping the maximum accepted error as 0.05 and beta as 0.2. An independent sample T-test is done, which reveals that the probability of handover failure (p) is 0.01 (p < 0.05), which is found to be statistically significant in the MCHVD approach when compared with the conventional RSS scheme. The results show that the handover failure rate for MCHVD is significantly lower than that for conventional RSS-based methods with respect to 20 iterations. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： RSS UMTS Vertical handover Wi-Fi WiMAX Wireless networks

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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IEEE communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Variable Bitrate Models For Learned Image Compression with Multi-gain units and Weighted Probability Assignment

Variable Bitrate Models For Learned Image Compression with M...

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IEEE Visual communications and Image Processing (VCIP)

作者： Ran Wang Wen Jiang Heming Sun Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Faculty of Engineering Yokohama National University Japan Department of Computer Science and Communication Engineering Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

With the advancement of deep learning techniques, learned image compression (LIC) has surpassed traditional compression methods. However, these methods typically require training separate models to achieve optimal rate-distortion performance, leading to increased time and resource consumption. To tackle this challenge, we propose leveraging multi-gain and inverse multi-gain unit pairs to enable variable rate adaptation within a single model. Nevertheless, experiments have shown that rate-distortion performance may degrade at certain bitrates. Therefore, we introduce weighted probability assignment, where different selection probabilities are assigned during training based on lambda values, to increase the model’s training frequency under specific bitrate conditions. To validate our approach, extensive experiments were conducted on Transformer-based and CNN-based models. The experimental results validate the efficiency of our proposed method.

关键词： Training Degradation Deep learning Adaptation models Image coding Visual communication Bit rate Redundancy Rate-distortion Transformers

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Deployment of neural networks for WLAN thumbprint indoor positioning system

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

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Wireless Liberty: Sensors Measuring Pressure and Temperature

Wireless Liberty: Sensors Measuring Pressure and Temperature

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2023 International Conference on Advances in Computation, communication and Information Technology, ICAICCIT 2023

作者： Chaudhary, Pavan Vanithalakshmi, M. Raja Praveen, K.N. Maharishi University of Information Technology School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology. © 2023 IEEE.

关键词： Continuous monitoring and Health outcomes Healthcare NFC technology Physiological monitoring Pressure sensing element Pressure ulcers Skin temperature Temperature measurement Wireless sensors

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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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Patient-Centric Skill Evaluation in IoT-Based Surgical Training with Naive Bayes Algorithm for Personalized Feedback 2

Patient-Centric Skill Evaluation in IoT-Based Surgical Train...

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2nd International Conference on computer, communication and Control, IC4 2024

作者： Gopi, B. Ashfaqul Hasan, M.D. Kumar, G. Sivakumar, S. Raju, M. Janardhana Malathi, N. Department of Electronics and Communication Engineering Rasipuram Tamil Nadu India Narsampet Telangana Warangal India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Management Studies Kattankulathur Chenagalpattu Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Siddartha Institute of Science and Technology Narayanavanam Andhra Pradesh Puttur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Internet of Things (IoT) technology has established essential in advancing surgical training, enabling more individualized and patient-centric methods. This paper proposes a unique framework for skill assessment in surgical training, concentrating on individualized feedback using the Naive Bayes algorithm. With the use of IoT technology, surgical simulators can now collect real-time data on trainee performance while also communicating patient-centric factors into account. The data is analyzed using the suggested Naive Bayes Algorithm, which places an emphasis on providing personalized feedback to individuals. The objective of this method is to improve surgical outcomes while simultaneously increasing surgeons' technical competence. The trainee's abilities are evaluated in a variety of ways by the algorithm, which takes into account accuracy, recall, and other metrics. It provides to the continuing attempts to establish more efficient and individualized training procedures using IoT-based surgical training combined with machine learning approaches. These results demonstrate the promise of the Naive Bayes algorithm for improving surgical outcomes via the delivery of actionable, personalized feedback to surgeons. © 2024 IEEE.

关键词： Internet of things

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