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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Sujatha, N. Pramod, S. Aravindh, S. Babu, Balagopala Ramesh devi, S. Rukmani Ananthi, M. B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Department of Mathematics Karnataka Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India SRKR Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamilnadu Chennai India KGiSL Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350354171

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. Mobile phones have practically become people's constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer's biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid lSTM-Ensemble learning. The average accuracy of the suggested method is 92.76%, which is higher than lSTM and Ensemble learning. © 2024 IEEE.

关键词： Contrastive learning

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Synergizing SVM Classifier with Hybrid CNN Architecture for Handwritten Recognition 6

Synergizing SVM Classifier with Hybrid CNN Architecture for ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sujay, V. lakshmi, B. Seetha Venkatesan, T. Ram, S Sakthiya Sangeetha, E. Satheeshkumar, S. Krishna University College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Machilipatnam India Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Namakkal India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India Bannari Amman Institute of Technology Department of ECE Tamil Nadu Erode India Department of ECE Tamil Nadu Namakkal India

ISBN: (纸本)9798350306613

The integration of Hybrid Convolutional Neural Network (CNN) architecture with a Support Vector Machine (SVM) classifier is proposed in the study as an innovative technique for Handwritten Recognition. The necessity for the research originates from existing systems' limitations in effectively understanding handwritten material due to their dependence on basic models. Existing system limitations included low accuracy, particularly in the presence of variances in writing styles and situations. The proposed system takes advantage of CNN's feature extraction and representation capabilities and SVM's discriminative power in classification. The hybrid design improves the model's capacity to grasp complicated patterns in handwritten input, overcoming existing system limitations. The results show a considerable improvement in recognition accuracy after extensive testing and analysis, confirming the effectiveness of the proposed system. The results demonstrate the system's durability in dealing with a wide range of handwritten inputs, making it a potential alternative for real-world applications requiring accuracy and flexibility. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Feature Extraction Handwritten Recognition Hybrid Architecture Recognition Accuracy Support Vector Machine (SVM) Classifier Writing Style

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IoT Applications in Cold Chain Management for Pharmaceuticals: Ensuring Product Integrity and Safety 5

IoT Applications in Cold Chain Management for Pharmaceutical...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Panigrahi, Bhawani Sankar Vanitha, A. Palav, Manesh R. Tilak Babu, S.B.G. Nair, Amrutha Muralidharan Bogiri, Nagaraju Vardhaman College of Engineering Department of IT Telangana Hyderabad India Saveetha Engineering College Department of Management Studies Chennai India Dr. D. Y. Patil Vidyapeeth Global Business School & Research Centre Department of MBA Maharashtra Pune India Aditya Engineering College Department of ECE Surampalem India Adi Shankara Institute of Engineering and Technology Department of CSE AI Kerala Ernakulam India Vishwakarma Institute of Information Technology Department of Computer Engineering Maharastra Pune India

ISBN: (纸本)9798350351378

The Cold Chain refers to the process of transporting and storing temperature-sensitive products within a specified temperature range, ensuring that these products maintain their efficacy and safety from manufacturing to end-users. This research explores the various IoT applications that contribute to the optimization of cold chain management, specifically focusing on pharmaceuticals. IoT-enabled devices, sensors, and data analytics play a pivotal role in real-time monitoring, control, and management of temperature-sensitive pharmaceuticals during their entire journey through the supply chain. In conclusion, the adoption of IoT applications in cold chain management for pharmaceuticals represents a paradigm shift in ensuring product integrity and safety. The real-time monitoring, predictive analytics, and enhanced visibility provided by IoT contribute to the overall efficiency of the pharmaceutical supply chain, ultimately safeguarding the well-being of patients and upholding the industry's commitment to delivering high-quality healthcare products. © 2024 IEEE.

关键词： Internet of things

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Adaptive residual convolutional neural network for moiré image restoration

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International Journal of Information Technology (Singapore) 2025年第2期17卷 783-791页

作者： Malagi, Vindhya P. Naresh, E. Mithra, C. Suresh, B. V. N. V. Krishna Department of AI & amp ML Dayananda Sagar College of Engineering Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Computer Science & amp Engineering KLEF KL University Andhra Pradesh Vaddeswaram India

Restoring Moiré images presents significant challenges due to the complex interference patterns that obscure image details. These patterns often degrade the quality of images, making accurate restoration crucial for various applications. Effective Moiré image restoration requires advanced techniques to overcome the difficulties posed by these intricate artifacts. This study introduces an Adaptive Residual Convolutional Neural Network (RCNN) for Moiré image restoration, augmented with the Zebra Optimization Algorithm (ZOA) to enhance both feature extraction and restoration accuracy. The hybrid model leverages ZOA's capability to balance exploration and exploitation, optimizing network parameters to improve learning efficiency. Tested on the dIV2K dataset, this approach demonstrates significant improvements in handling complex Moiré patterns, as evidenced by enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. © The Author(s) 2024.

关键词： Adaptive residual convolutional neural network Moiré images Peak signal-to-noise ratio Structural similarity index measure Zebra optimization algorithm

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deep Reinforcement learning for Addressing Interoperability Challenges in disaster Management Systems 2

Deep Reinforcement Learning for Addressing Interoperability ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Hemamalini, V. Ramesh, Gajula Varshney, Neeraj Kumar, G. Pavan Ajzan Balassem, Zaid Aravind, T. New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamilnadu Chennai600073 India GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Department of CSE CMR Technical Campus Telangana Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350368109

This research sheds light toward the use of deep Reinforcement learning (dRl) strategy to solve interoperability issues in disaster management systems. The overall performance of the proposed dRl-based framework is deemed to be superior to the conventional rule-based systems in terms of response time, coordination efficiency, and resource utilization, clocking up the enhancements of 33%, 20% and 20%, respective. By way of simulation and actual re-Creation to emerge as a proper model-based planning format for successful disaster management, the efficacy of dRl framework is evident. It also aligns well with the concern by pointing out that dRl holds the potential for revolutionizing the process of disaster management and presents a sound efficient and effective solution necessary for the management of the modern difficulties. © 2024 IEEE.

关键词： deep reinforcement learning

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An Innovative IoT-driven Smart Streetlight Management System design 2

An Innovative IoT-Driven Smart Streetlight Management System...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shermina Justin, Shekaina Thenmozhi, P. George, Jissy Ann Kumar, T. Ananth Ananth, Christo Muscat College Department of Computing Oman Princess Nourah Bint Abdulrahman University Electrical Engineering Department Saudi Arabia Dept. of Electronics and Communication Engineering Chennai600119 India British University of Bahrain College of Ict Bahrain Ifet College of Engineering Computer Science and Engineering Viluppuram India Samarkand State University Uzbekistan Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350364699

This research introduces a cutting-edge approach to optimizing the management of streetlight systems through automation by using IoT. Traditional methods often suffer from inefficiencies, leading to unnecessary energy consumption and maintenance costs. In this study, we propose a novel automated streetlight management system that leverages advanced technologies such as IoT sensors, machine learning algorithms, and smart scheduling mechanisms. By dynamically adjusting lighting levels based on real-time factors such as traffic density, weather conditions, and time of day, our system aims to achieve significant energy savings while ensuring optimal illumination for pedestrians and vehicles. We present the design architecture of our system, along with experimental results demonstrating its effectiveness in various urban environments. Our findings highlight the potential of automated streetlight management systems to enhance sustainability, improve safety, and reduce operational expenses for municipalities and urban planners. © 2024 IEEE.

关键词： Energy utilization

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Secure and Responsive Human-Robot Interaction for dementia Patients Using lSTM and IoT 3

Secure and Responsive Human-Robot Interaction for Dementia P...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ramya, S. Radhika, M. Roychoudri, Subramani Senthil, S. Arunachalam, G. Muthulekshmi, M. Paavai Engineering College Department of Cyber Security Tamil Nadu Namakkal India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Usha Rama College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Mathematics Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

An innovative architecture that combines long Short-Term Memory (lSTM) networks with Internet of Things (IoT) technology is presented in this paper to assist dementia patients. It solves Human-Robot Interaction (HRI) difficulties in dementia care. It also ensures security and responsiveness in HRI to establish a secure and trustworthy environment for dementia patients. lSTM networks help the robot to recognize and to adapt based on the dementia patients' behaviors. Through real-time data gathering and exchange, IoT devices offer seamless and context-aware interactions. The system protects user privacy and sensitive data with strong encryption and authentication processes. The framework uses adaptive learning methods to improve the robot's comprehension of the user's preferences and needs, offering a personalized and responsive assistance system. Simulations and real-world situations show that the proposed technology provides safe, responsive, and personalized human-robot interactions for dementia care. By presenting a potential solution to dementia care difficulties it advances assistive technology and improves quality of life for dementia patients. © 2024 IEEE.

关键词： Human robot interaction

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Fisherman location Store, Updating, and Alert System using Cloud AWS with Satellite

Fisherman Location Store, Updating, and Alert System using C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Kiruthiga devi, M. Parthasarathy, R. Unnamalai, K. Perinba Athiban, S. Manikandan, K. Ramya, N. Dr. MGR Educational and Research Institute Chennai Department of Information Technology India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Adarsh College for Women Department of Computer Science Anna India VIT Artificial Intelligence and Robotics Chennai India Sona College of Technology Department of Biomedical Engineering Salem India Sri Sai Ram Engineering College Department of CSE Chennai India

ISBN: (纸本)9798331522988

he successful demonstration of their location tracking alert system for fishermen, Satellites will employ Amazon Web Services (AWS). Using cloud-based computing technology, the activities and whereabouts of fishermen are monitored in real time through this system. The ACS sat will send a vast amount of information in short always-shrinking cycle and store data very efficiently, only the location data for live updates on every moves by the fishermen. Here using this project we are stressing on the need to analyze data and push alerts according protocols, for timely raise alarm which definitely saves Fishermen life. The aim is to develop a Real Time location Tracking and Monitoring GIS Application, which can trace the fishermen real-time location in marine activities. Analyze location information with the best edge of the sword such as cloud computing and AI /Ml. The GIS App will have a simple user interface along with extensive monitoring abilities. Building a GIS App to aid marine safety by improving the tracking and monitoring of fishing operations using cloud technology. In the end, with these benchmarks in tow this could mark a step change and benchmark for maritime operations;as we finally get, safety and productivity combined. © 2024 IEEE.

关键词： Benchmarking

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Multi-task Convolutional Neural Network Approach for Automatic Alzheimer disease Classification 3

Multi-task Convolutional Neural Network Approach for Automat...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Zope, Minal A. deshmukh, Rakesh K. Pise, Priya Bhopale, Shrikant d. Kalinga University Raipur India AISSMS's Institute of Information Technology Dept. of Computer Engineering Pune India Indira College of Engineering and Management Artificial Intelligence and Data Science Pune India Dr.D.Y.Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (纸本)9798350381931

While CNN-based Alzheimer's disease (Ad) classification has showed some encouraging results, it will need high accuracy, fast processing time, and generalizability to a number of population types before it can be used in clinical settings. The brain disorder known as Alzheimer's disease (Ad) is progressive and currently has no cure. Alzheimer's disease (Ad) is characterized by a decline in memory and other mental abilities as well as a loss of autonomy in daily life. The results of this study suggest that using statistical learning and ensemble learning strategies, an image feature extraction technique like the Auto color correlogram Filter methods may be used successfully to an Alzheimer's photos dataset. dementia caused by Alzheimer's disease (Ad) is a progressive form of the neurodegenerative brain disorder. It's not always obvious at first since there are usually so few symptoms at the outset, and they might be similar to those of other disorders. distinguishing Alzheimer's disease from mild cognitive deterioration, which may be appreciated in normal aging, may be challenging. © 2024 IEEE.

关键词： Convolutional neural networks

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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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